{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 一起来和AI猜拳\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 一、项目背景\n",
    "目前人工智能发展迅速，为普通民众以及青少年普及相关人工智能的知识。项目采取人们易于接受的游戏形式，通过和AI的猜拳对弈，将相关的技术知识融入其中，最终可以实现让参与者在玩中学习，既是一种家庭娱乐方式，也是学习的一个机会。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 二、数据集简介\n",
    "项目数据集主要是图片数据，通过相机捕捉获得，其中剪刀手势一共720张，石头手势一共696张，布手势一共680张。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.数据加载和预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!unzip /home/aistudio/work/hand.zip -d ./"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "剪刀一共720张，石头一共696张，布一共680张\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pi2Rmtp8o9RLTcmBDnoWYmVn7UuoZxHqgUdJTFKb8BjzM1cxsX1ZqQPxbepiZ2X6i1O+DmNZyLzMz25eU+knqFWTMvRQRHsVkZraPKvUSU13R8qcpTKrnz0GYme3DShrFFBFvFz2aImIShe+aNjOzfVSpl5hqi1Y/ReGMotSzDzMz2wuV+p/8L/noHsQm4A/4uxvMzPZppQbEMODrbD/d9yXAj3OoyczM2oHWfA5iLTAf+CC/cszMrL0oNSBqImJorpWYmVm7UupcTM9J6pdrJWZm1q6UGhBfBOZJWiJpkaTFkhbtagdJn5b0gqSFkl6W9KPUfoyk5yUtl/Svkjqm9gPT+vK0vcfuvDAzM9s9rblJ3VofAmdExPuSDgBmS3oMuBaYGBEzJP13YCwwOT3/OSJ6SroEmABc/Al+rpmZ7QGlflDujaxHC/tERLyfVg9IjwDOAO5P7dOA89Py8LRO2n6mJLXitZiZ2R5U6iWmT0RSB0mNwBpgFvDvwNqI2JS6rAKq03I1sBIgbV8HHJFnfWZmtnO5BkREbI6IAUANcArQe3ePKWmcpLmS5jY3N+92jWZmli3XgNgqItYCTwGnApWStt77qAGa0nIT0B0gbT8ceDvjWFMioi4i6qqqqnKv3cxsf5VbQEiqklSZlg8CBgOvUgiKEanbaGBmWq5P66TtT0bEx6YYNzOztpHnhHtdgWmSOlAIovsi4mFJrwAzJN0ELADuTP3vBO6RtBx4h8JUHmZmVia5BURELAJOzGh/ncL9iB3bP8ATAJqZtRttcg/CzMz2Pg4IMzPL5IAwM7NMDggzM8vkgDAzs0wOCDMzy+SAMDOzTA4IMzPL5IAwM7NMDggzM8vkgDAzs0wOCDMzy+SAMDOzTA4IMzPL5IAwM7NMDggzM8vkgDAzs0wOCDMzy+SAMDOzTA4IMzPLlFtASOou6SlJr0h6WdLVqb2zpFmSlqXnTqldkm6VtFzSIkm1edVmZmYty/MMYhPwnYjoAwwErpDUB7gBaIiIXkBDWgcYBvRKj3HA5BxrMzOzFuQWEBGxOiLmp+X3gFeBamA4MC11mwacn5aHA3dHwRygUlLXvOozM7Nda5N7EJJ6ACcCzwNdImJ12vQm0CUtVwMri3Zbldp2PNY4SXMlzW1ubs6tZjOz/V3uASHpM8ADwDUR8W7xtogIIFpzvIiYEhF1EVFXVVW1Bys1M7NiuQaEpAMohMO9EfFgan5r66Wj9LwmtTcB3Yt2r0ltZmZWBnmOYhJwJ/BqRPxT0aZ6YHRaHg3MLGoflUYzDQTWFV2KMjOzNlaR47G/APwdsFhSY2r7PjAeuE/SWOAN4KK07VHgHGA5sAEYk2NtZmbWgtwCIiJmA9rJ5jMz+gdwRV71mJlZ6/iT1GZmlskBYWZmmRwQZmaWyQFhZmaZHBBmZpbJAWFmZpkcEGZmlskBYWZmmRwQZmaWyQFhZmaZHBBmZpbJAWFmZpkcEGZmlskBYWZmmRwQZmaWyQFhZmaZHBBmZpbJAWFmZpkcEGZmlim3gJA0VdIaSS8VtXWWNEvSsvTcKbVL0q2SlktaJKk2r7rMzKw0eZ5B3AUM3aHtBqAhInoBDWkdYBjQKz3GAZNzrMvMzEqQW0BExLPAOzs0DwempeVpwPlF7XdHwRygUlLXvGozM7OWtfU9iC4RsTotvwl0ScvVwMqifqtSm5mZlUnZblJHRADR2v0kjZM0V9Lc5ubmHCozMzNo+4B4a+ulo/S8JrU3Ad2L+tWkto+JiCkRURcRdVVVVbkWa2a2P2vrgKgHRqfl0cDMovZRaTTTQGBd0aUoMzMrg4q8DixpOvBl4EhJq4AbgfHAfZLGAm8AF6XujwLnAMuBDcCYvOoyM7PS5BYQETFyJ5vOzOgbwBV51WJmZq3nT1KbmVkmB4SZmWVyQJiZWSYHhJmZZXJAmJlZJgeEmZllckCYmVkmB4SZmWVyQJiZWSYHhJmZZXJAmJlZJgeEmZllckCYmVkmB4SZmWVyQJiZWSYHhJmZZXJAmJlZJgeEmZllckCYmVkmB4SZmWVqVwEhaaikJZKWS7qh3PWYme3P2k1ASOoA3A4MA/oAIyX1KW9VZmb7r3YTEMApwPKIeD0iNgIzgOFlrsnMbL/VngKiGlhZtL4qtZmZWRlUlLuA1pI0DhiXVt+XtKSc9XxCRwJ/ausfqglt/RP3a2V5j8HvcxvbW9/no0vp1J4CognoXrRek9q2ExFTgCltVVQeJM2NiLpy12H58Xu8f9jX3+f2dInpRaCXpGMkdQQuAerLXJOZ2X6r3ZxBRMQmSVcCvwU6AFMj4uUyl2Vmtt9qNwEBEBGPAo+Wu442sFdfIrOS+D3eP+zT77Miotw1mJlZO9Se7kGYmVk74oDIiaQ6SbeWuw5rXyT9UNJ15a7DrBTt6h7EviQi5gJz8zi2pA4RsTmPY1vrSBKFS7Vbyl2L7T0kVUTEpnLX0RKfQbSSpEMkPSJpoaSXJF0s6WRJz6W2FyQdKunLkh5O+5wmqTE9FqTtXSU9m9pekvSl1HekpMWpbULRz31f0i8lLQROLdPLN0BSjzSp5N3AS8Df7eQ9Gyppfvq9aMg4zt9LekzSQW1Zv2VL7+trku6V9Kqk+yUdLOm/Snoxvb9T0h8FSHpa0i1F/4ZPSe2HSJqa/i9YIGl4av+GpHpJTwIf+31olyLCj1Y8gK8DdxStHw68Dpyc1g+jcGb2ZeDh1Pa/gC+k5c+k7d8BfpDaOgCHAt2A/wtUpT5PAuenPgFcVO7X70cA9AC2AAN39p6l9ZXAMWmfzun5h8B1wJXATODAcr8eP7Z7X6Po3+rU9F51LupzD3BuWn566/8FwCDgpbT8j8B/TMuVwFLgEOAbFKYQ6twWr2dPPHwG0XqLgcGSJqS/+o8CVkfEiwAR8W58/NTxd8A/SboKqEzbXwTGSPoh0C8i3gNOBp6OiObU514Kv3gAm4EH8n5xVrI3ImIOO3/PBgLPRsQKgIh4p2jfURRmLR4RER+2cd22aysj4ndp+V+ALwKnS3pe0mLgDKBvUf/pABHxLHCYpErgbOAGSY0UQuTTFP6fAJi1w+9Cu+aAaKWIWArUUgiKm4ALSthnPHAZcBDwO0m90y/UIArTidwlaVQLh/kgfN+hPVm/G/supvDXas2eKcX2oB3H/QfwKwph3g+4g8J/+LvqL+DrETEgPY6KiFfT9t35vWlzDohWktQN2BAR/wL8HPg80FXSyWn7oZIqdtjn2IhYHBETKJw59JZ0NPBWRNwB/JpC6LwAnCbpyPT9GCOBZ9rsxdknsbP3bA4wSNIxAJI6F+2zAPjPQH36fbL24yhJW+/x/S0wOy3/SdJngBE79L8YQNIXgXURsY7CbBDfLrpXcWL+ZefDo5harx/wc0lbgL8Al1P4i+G2dLPx/wFn7bDPNZJOp3Dd+mXgMQpzTX1X0l+A94FREbE6fZPeU+mYj0TEzLZ4UfbJ7Oo9SzMPPyjpU8AaYHDRfrPTcNdHJA2OiLLMCGofswS4QtJU4BVgMtCJwmCENyn8gVfsA0kLgAOA/5TafgJMAhal934F8NU2qH2P8yepzcwojGKiMLDkb0rs/zRwXRSGtO+TfInJzMwy+QzCzMwy+QzCzMwyOSDMzCyTA8LMzDI5IMxKlObqeSmH494lacfx9WZl54AwM7NMDgiz1ukg6Q5JL0t6QtJBaVbWF9OsrQ9IOhi2nRncmmb6fX3rWYIK/luaEfZ/A39V1ldkthMOCLPW6QXcHhF9gbUUZvd9MCJOjogTgFeBsUX9u1KY8O2rwPjU9jXgOKAPhYn7/kMb1W7WKp5qw6x1VkREY1qeR2HSvb+RdBOFqZ0/Q2Eunq3+LQpfJvSKpC6pbRAwPU2++Mf0/QBm7Y7PIMxap3h67s0U/si6C7gyzfb5I7af7bO4v3KvzmwPckCY7b5DgdWSDgAuLaH/s8DFkjpI6gqcnmt1Zp+QLzGZ7b7/AjwPNKfnQ1vo/xCFL555hcK30f0+1+rMPiHPxWRmZpl8icnMzDI5IMzMLJMDwszMMjkgzMwskwPCzMwyOSDMzCyTA8LMzDI5IMzMLNP/B1DfjQ9K9QVxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "paper = os.listdir(\"hand/paper\")\r\n",
    "rock = os.listdir(\"hand/rock\")\r\n",
    "scissor = os.listdir(\"hand/scissor\")\r\n",
    "labels=[\"scissor\",\"rock\",\"paper\"]\r\n",
    "\r\n",
    "pl=[len(scissor),len(rock),len(paper)]\r\n",
    "plt.rcParams['font.sans-serif']=[\"STSong\"]\r\n",
    "plt.xlabel(\"hand\")\r\n",
    "plt.ylabel(\"number\")\r\n",
    "x=range(len(labels))\r\n",
    "plt.xticks(x,labels)\r\n",
    "plt.bar(x,pl,width=0.5)\r\n",
    "print('剪刀一共{}张，石头一共{}张，布一共{}张'.format(len(scissor),len(rock),len(paper)))\r\n",
    "for x,y in zip(x,pl):\r\n",
    "    plt.text(x,y+10,'%.0f'%y,ha=\"center\",va=\"center\")\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.数据集查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
      "Collecting paddlex\n",
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      "Building wheels for collected packages: pycocotools\n",
      "  Building wheel for pycocotools (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=278372 sha256=c6e423392c7a995cbe4a12abadde5b608ca0fa13bdc46f7194c9c3f2cfda398b\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/fb/44/67/8baa69040569b1edbd7776ec6f82c387663e724908aaa60963\n",
      "Successfully built pycocotools\n",
      "Installing collected packages: shapely, xlwt, pycocotools, paddleslim, paddle2onnx, paddlehub, paddlex\n",
      "  Found existing installation: paddlehub 2.0.4\n",
      "    Uninstalling paddlehub-2.0.4:\n",
      "      Successfully uninstalled paddlehub-2.0.4\n",
      "Successfully installed paddle2onnx-0.7 paddlehub-2.1.0 paddleslim-1.1.1 paddlex-1.3.11 pycocotools-2.0.2 shapely-1.7.1 xlwt-1.3.0\n"
     ]
    }
   ],
   "source": [
    "!pip install paddlex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Split Done.\u001b[0m\r\n",
      "\u001b[0mTrain samples: 1468\u001b[0m\r\n",
      "\u001b[0mEval samples: 419\u001b[0m\r\n",
      "\u001b[0mTest samples: 209\u001b[0m\r\n",
      "\u001b[0mSplit files saved in hand\u001b[0m\r\n",
      "\u001b[0m\u001b[0m"
     ]
    }
   ],
   "source": [
    "!paddlex --split_dataset --format ImageNet --dataset_dir hand --val_value 0.2 --test_value 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、模型选择与开发"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.加载模型库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from paddlex.cls import transforms\r\n",
    "import os\r\n",
    "import cv2\r\n",
    "import numpy as np\r\n",
    "import paddlex as pdx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.数据增强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_transforms = transforms.Compose([\r\n",
    "    transforms.RandomCrop(crop_size=224), #随机裁剪\r\n",
    "    transforms.RandomHorizontalFlip(),#随机水平翻转，概率默认为0.5\r\n",
    "    transforms.RandomVerticalFlip(),#随机垂直翻转，概率默认为0.5\r\n",
    "    transforms.RandomRotate(rotate_range=90),#以0.5的概率对图像在[-90, 90]角度范围内进行旋转\r\n",
    "    transforms.Normalize()#对图像进行标准化处理\r\n",
    "])\r\n",
    "eval_transforms = transforms.Compose([\r\n",
    "    transforms.ResizeByShort(short_size=256),#根据图像的短边调整图像大小\r\n",
    "    transforms.CenterCrop(crop_size=224), #以图像中心点扩散裁剪正方形\r\n",
    "    transforms.Normalize() #对图像进行标准化\r\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:03 [INFO]\tStarting to read file list from dataset...\n",
      "2021-08-12 19:44:03 [INFO]\t1468 samples in file hand/train_list.txt\n",
      "2021-08-12 19:44:03 [INFO]\tStarting to read file list from dataset...\n",
      "2021-08-12 19:44:03 [INFO]\t419 samples in file hand/val_list.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/framework.py:706: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif dtype == np.bool:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:04 [INFO]\tDownloading ResNet18_pretrained.tar from https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 45820/45820 [00:01<00:00, 43471.67KB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:05 [INFO]\tDecompressing output/result/pretrain/ResNet18_pretrained.tar...\n",
      "2021-08-12 19:44:11 [INFO]\tLoad pretrain weights from output/result/pretrain/ResNet18_pretrained.\n",
      "2021-08-12 19:44:11 [WARNING]\t[SKIP] Shape of pretrained weight output/result/pretrain/ResNet18_pretrained/fc_0.w_0 doesn't match.(Pretrained: (512, 1000), Actual: (512, 3))\n",
      "2021-08-12 19:44:11 [WARNING]\t[SKIP] Shape of pretrained weight output/result/pretrain/ResNet18_pretrained/fc_0.b_0 doesn't match.(Pretrained: (1000,), Actual: (3,))\n",
      "2021-08-12 19:44:11 [INFO]\tThere are 105 varaibles in output/result/pretrain/ResNet18_pretrained are loaded.\n",
      "2021-08-12 19:44:15 [INFO]\t[TRAIN] Epoch=1/20, Step=2/22, loss=1.08433, acc1=0.453125, acc3=1.0, lr=0.003, time_each_step=2.07s, eta=0:19:55\n",
      "2021-08-12 19:44:16 [INFO]\t[TRAIN] Epoch=1/20, Step=4/22, loss=0.938204, acc1=0.59375, acc3=1.0, lr=0.003, time_each_step=1.11s, eta=0:10:41\n",
      "2021-08-12 19:44:16 [INFO]\t[TRAIN] Epoch=1/20, Step=6/22, loss=0.94028, acc1=0.484375, acc3=1.0, lr=0.003, time_each_step=0.79s, eta=0:7:33\n",
      "2021-08-12 19:44:16 [INFO]\t[TRAIN] Epoch=1/20, Step=8/22, loss=0.841812, acc1=0.6875, acc3=1.0, lr=0.003, time_each_step=0.63s, eta=0:6:1\n",
      "2021-08-12 19:44:17 [INFO]\t[TRAIN] Epoch=1/20, Step=10/22, loss=0.752892, acc1=0.609375, acc3=1.0, lr=0.003, time_each_step=0.54s, eta=0:5:5\n",
      "2021-08-12 19:44:17 [INFO]\t[TRAIN] Epoch=1/20, Step=12/22, loss=0.788967, acc1=0.671875, acc3=1.0, lr=0.003, time_each_step=0.47s, eta=0:4:27\n",
      "2021-08-12 19:44:17 [INFO]\t[TRAIN] Epoch=1/20, Step=14/22, loss=0.447981, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.42s, eta=0:4:0\n",
      "2021-08-12 19:44:18 [INFO]\t[TRAIN] Epoch=1/20, Step=16/22, loss=0.559329, acc1=0.765625, acc3=1.0, lr=0.003, time_each_step=0.39s, eta=0:3:40\n",
      "2021-08-12 19:44:18 [INFO]\t[TRAIN] Epoch=1/20, Step=18/22, loss=0.325249, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.36s, eta=0:3:24\n",
      "2021-08-12 19:44:18 [INFO]\t[TRAIN] Epoch=1/20, Step=20/22, loss=0.276215, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.34s, eta=0:3:12\n",
      "2021-08-12 19:44:19 [INFO]\t[TRAIN] Epoch=1/20, Step=22/22, loss=0.498904, acc1=0.8125, acc3=1.0, lr=0.003, time_each_step=0.16s, eta=0:1:29\n",
      "2021-08-12 19:44:19 [INFO]\t[TRAIN] Epoch 1 finished, loss=0.697252, acc1=0.710227, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:44:19 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/7 [00:00<?, ?it/s]share_vars_from is set, scope is ignored.\n",
      "100%|██████████| 7/7 [00:03<00:00,  2.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:22 [INFO]\t[EVAL] Finished, Epoch=1, acc1=0.959427, acc3=1.0 .\n",
      "2021-08-12 19:44:22 [INFO]\tModel saved in output/result/best_model.\n",
      "2021-08-12 19:44:22 [INFO]\tModel saved in output/result/epoch_1.\n",
      "2021-08-12 19:44:22 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_1, acc1=0.9594272076372315\n",
      "2021-08-12 19:44:24 [INFO]\t[TRAIN] Epoch=2/20, Step=2/22, loss=0.198445, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:3:27\n",
      "2021-08-12 19:44:25 [INFO]\t[TRAIN] Epoch=2/20, Step=4/22, loss=0.218488, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:26\n",
      "2021-08-12 19:44:25 [INFO]\t[TRAIN] Epoch=2/20, Step=6/22, loss=0.29793, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:26\n",
      "2021-08-12 19:44:25 [INFO]\t[TRAIN] Epoch=2/20, Step=8/22, loss=0.212384, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:25\n",
      "2021-08-12 19:44:26 [INFO]\t[TRAIN] Epoch=2/20, Step=10/22, loss=0.176667, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:25\n",
      "2021-08-12 19:44:26 [INFO]\t[TRAIN] Epoch=2/20, Step=12/22, loss=0.295307, acc1=0.890625, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:24\n",
      "2021-08-12 19:44:26 [INFO]\t[TRAIN] Epoch=2/20, Step=14/22, loss=0.271066, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:24\n",
      "2021-08-12 19:44:27 [INFO]\t[TRAIN] Epoch=2/20, Step=16/22, loss=0.116674, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:23\n",
      "2021-08-12 19:44:27 [INFO]\t[TRAIN] Epoch=2/20, Step=18/22, loss=0.262177, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.25s, eta=0:3:23\n",
      "2021-08-12 19:44:27 [INFO]\t[TRAIN] Epoch=2/20, Step=20/22, loss=0.155461, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:3:22\n",
      "2021-08-12 19:44:28 [INFO]\t[TRAIN] Epoch=2/20, Step=22/22, loss=0.192279, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.16s, eta=0:3:22\n",
      "2021-08-12 19:44:28 [INFO]\t[TRAIN] Epoch 2 finished, loss=0.247597, acc1=0.913352, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:44:28 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  2.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:31 [INFO]\t[EVAL] Finished, Epoch=2, acc1=0.995227, acc3=1.0 .\n",
      "2021-08-12 19:44:31 [INFO]\tModel saved in output/result/best_model.\n",
      "2021-08-12 19:44:32 [INFO]\tModel saved in output/result/epoch_2.\n",
      "2021-08-12 19:44:32 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_2, acc1=0.9952267303102625\n",
      "2021-08-12 19:44:34 [INFO]\t[TRAIN] Epoch=3/20, Step=2/22, loss=0.166817, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:46\n",
      "2021-08-12 19:44:34 [INFO]\t[TRAIN] Epoch=3/20, Step=4/22, loss=0.386725, acc1=0.8125, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:45\n",
      "2021-08-12 19:44:34 [INFO]\t[TRAIN] Epoch=3/20, Step=6/22, loss=0.063192, acc1=1.0, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:45\n",
      "2021-08-12 19:44:34 [INFO]\t[TRAIN] Epoch=3/20, Step=8/22, loss=0.096994, acc1=0.984375, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:44\n",
      "2021-08-12 19:44:35 [INFO]\t[TRAIN] Epoch=3/20, Step=10/22, loss=0.322432, acc1=0.875, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:44\n",
      "2021-08-12 19:44:35 [INFO]\t[TRAIN] Epoch=3/20, Step=12/22, loss=0.251693, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:43\n",
      "2021-08-12 19:44:35 [INFO]\t[TRAIN] Epoch=3/20, Step=14/22, loss=0.15274, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:43\n",
      "2021-08-12 19:44:36 [INFO]\t[TRAIN] Epoch=3/20, Step=16/22, loss=0.174951, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:42\n",
      "2021-08-12 19:44:36 [INFO]\t[TRAIN] Epoch=3/20, Step=18/22, loss=0.155122, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:42\n",
      "2021-08-12 19:44:36 [INFO]\t[TRAIN] Epoch=3/20, Step=20/22, loss=0.233972, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.24s, eta=0:2:41\n",
      "2021-08-12 19:44:37 [INFO]\t[TRAIN] Epoch=3/20, Step=22/22, loss=0.241256, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.17s, eta=0:2:41\n",
      "2021-08-12 19:44:37 [INFO]\t[TRAIN] Epoch 3 finished, loss=0.213762, acc1=0.916193, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:44:37 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:42 [INFO]\t[EVAL] Finished, Epoch=3, acc1=0.997613, acc3=1.0 .\n",
      "2021-08-12 19:44:42 [INFO]\tModel saved in output/result/best_model.\n",
      "2021-08-12 19:44:42 [INFO]\tModel saved in output/result/epoch_3.\n",
      "2021-08-12 19:44:42 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_3, acc1=0.9976133651551312\n",
      "2021-08-12 19:44:47 [INFO]\t[TRAIN] Epoch=4/20, Step=2/22, loss=0.104615, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.37s, eta=0:3:3\n",
      "2021-08-12 19:44:47 [INFO]\t[TRAIN] Epoch=4/20, Step=4/22, loss=0.067048, acc1=0.984375, acc3=1.0, lr=0.003, time_each_step=0.37s, eta=0:3:3\n",
      "2021-08-12 19:44:47 [INFO]\t[TRAIN] Epoch=4/20, Step=6/22, loss=0.195926, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.37s, eta=0:3:2\n",
      "2021-08-12 19:44:48 [INFO]\t[TRAIN] Epoch=4/20, Step=8/22, loss=0.182725, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.38s, eta=0:3:1\n",
      "2021-08-12 19:44:48 [INFO]\t[TRAIN] Epoch=4/20, Step=10/22, loss=0.147628, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.38s, eta=0:3:0\n",
      "2021-08-12 19:44:48 [INFO]\t[TRAIN] Epoch=4/20, Step=12/22, loss=0.399416, acc1=0.859375, acc3=1.0, lr=0.003, time_each_step=0.38s, eta=0:3:0\n",
      "2021-08-12 19:44:49 [INFO]\t[TRAIN] Epoch=4/20, Step=14/22, loss=0.073293, acc1=1.0, acc3=1.0, lr=0.003, time_each_step=0.38s, eta=0:2:59\n",
      "2021-08-12 19:44:49 [INFO]\t[TRAIN] Epoch=4/20, Step=16/22, loss=0.137227, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.37s, eta=0:2:58\n",
      "2021-08-12 19:44:49 [INFO]\t[TRAIN] Epoch=4/20, Step=18/22, loss=0.1947, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.37s, eta=0:2:57\n",
      "2021-08-12 19:44:49 [INFO]\t[TRAIN] Epoch=4/20, Step=20/22, loss=0.163537, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.36s, eta=0:2:57\n",
      "2021-08-12 19:44:50 [INFO]\t[TRAIN] Epoch=4/20, Step=22/22, loss=0.269925, acc1=0.890625, acc3=1.0, lr=0.003, time_each_step=0.16s, eta=0:2:56\n",
      "2021-08-12 19:44:50 [INFO]\t[TRAIN] Epoch 4 finished, loss=0.18401, acc1=0.933239, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:44:50 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:44:54 [INFO]\t[EVAL] Finished, Epoch=4, acc1=0.995227, acc3=1.0 .\n",
      "2021-08-12 19:44:54 [INFO]\tModel saved in output/result/epoch_4.\n",
      "2021-08-12 19:44:54 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_3, acc1=0.9976133651551312\n",
      "2021-08-12 19:44:57 [INFO]\t[TRAIN] Epoch=5/20, Step=2/22, loss=0.12515, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.29s, eta=0:3:12\n",
      "2021-08-12 19:44:58 [INFO]\t[TRAIN] Epoch=5/20, Step=4/22, loss=0.09112, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.29s, eta=0:3:12\n",
      "2021-08-12 19:44:58 [INFO]\t[TRAIN] Epoch=5/20, Step=6/22, loss=0.106712, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:11\n",
      "2021-08-12 19:44:58 [INFO]\t[TRAIN] Epoch=5/20, Step=8/22, loss=0.148249, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:11\n",
      "2021-08-12 19:44:58 [INFO]\t[TRAIN] Epoch=5/20, Step=10/22, loss=0.286518, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:10\n",
      "2021-08-12 19:44:59 [INFO]\t[TRAIN] Epoch=5/20, Step=12/22, loss=0.221478, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:10\n",
      "2021-08-12 19:44:59 [INFO]\t[TRAIN] Epoch=5/20, Step=14/22, loss=0.331892, acc1=0.875, acc3=1.0, lr=0.003, time_each_step=0.29s, eta=0:3:9\n",
      "2021-08-12 19:44:59 [INFO]\t[TRAIN] Epoch=5/20, Step=16/22, loss=0.118373, acc1=0.984375, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:8\n",
      "2021-08-12 19:45:00 [INFO]\t[TRAIN] Epoch=5/20, Step=18/22, loss=0.244135, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.28s, eta=0:3:8\n",
      "2021-08-12 19:45:00 [INFO]\t[TRAIN] Epoch=5/20, Step=20/22, loss=0.192875, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.27s, eta=0:3:7\n",
      "2021-08-12 19:45:00 [INFO]\t[TRAIN] Epoch=5/20, Step=22/22, loss=0.08835, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.15s, eta=0:3:7\n",
      "2021-08-12 19:45:01 [INFO]\t[TRAIN] Epoch 5 finished, loss=0.18403, acc1=0.935369, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:45:01 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:45:04 [INFO]\t[EVAL] Finished, Epoch=5, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:45:05 [INFO]\tModel saved in output/result/best_model.\n",
      "2021-08-12 19:45:05 [INFO]\tModel saved in output/result/epoch_5.\n",
      "2021-08-12 19:45:05 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:45:08 [INFO]\t[TRAIN] Epoch=6/20, Step=2/22, loss=0.163456, acc1=0.9375, acc3=1.0, lr=0.003, time_each_step=0.3s, eta=0:2:35\n",
      "2021-08-12 19:45:09 [INFO]\t[TRAIN] Epoch=6/20, Step=4/22, loss=0.138244, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:35\n",
      "2021-08-12 19:45:09 [INFO]\t[TRAIN] Epoch=6/20, Step=6/22, loss=0.066085, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:34\n",
      "2021-08-12 19:45:09 [INFO]\t[TRAIN] Epoch=6/20, Step=8/22, loss=0.126819, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:34\n",
      "2021-08-12 19:45:10 [INFO]\t[TRAIN] Epoch=6/20, Step=10/22, loss=0.19554, acc1=0.921875, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:33\n",
      "2021-08-12 19:45:10 [INFO]\t[TRAIN] Epoch=6/20, Step=12/22, loss=0.137994, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:32\n",
      "2021-08-12 19:45:10 [INFO]\t[TRAIN] Epoch=6/20, Step=14/22, loss=0.147512, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:32\n",
      "2021-08-12 19:45:10 [INFO]\t[TRAIN] Epoch=6/20, Step=16/22, loss=0.131092, acc1=0.953125, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:31\n",
      "2021-08-12 19:45:11 [INFO]\t[TRAIN] Epoch=6/20, Step=18/22, loss=0.188451, acc1=0.90625, acc3=1.0, lr=0.003, time_each_step=0.31s, eta=0:2:31\n",
      "2021-08-12 19:45:11 [INFO]\t[TRAIN] Epoch=6/20, Step=20/22, loss=0.275828, acc1=0.875, acc3=1.0, lr=0.003, time_each_step=0.32s, eta=0:2:30\n",
      "2021-08-12 19:45:12 [INFO]\t[TRAIN] Epoch=6/20, Step=22/22, loss=0.095891, acc1=0.96875, acc3=1.0, lr=0.003, time_each_step=0.17s, eta=0:2:29\n",
      "2021-08-12 19:45:12 [INFO]\t[TRAIN] Epoch 6 finished, loss=0.155987, acc1=0.93892, acc3=1.0, lr=0.003 .\n",
      "2021-08-12 19:45:12 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:45:16 [INFO]\t[EVAL] Finished, Epoch=6, acc1=0.997613, acc3=1.0 .\n",
      "2021-08-12 19:45:16 [INFO]\tModel saved in output/result/epoch_6.\n",
      "2021-08-12 19:45:16 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:45:20 [INFO]\t[TRAIN] Epoch=7/20, Step=2/22, loss=0.241897, acc1=0.890625, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:38\n",
      "2021-08-12 19:45:20 [INFO]\t[TRAIN] Epoch=7/20, Step=4/22, loss=0.14079, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:38\n",
      "2021-08-12 19:45:20 [INFO]\t[TRAIN] Epoch=7/20, Step=6/22, loss=0.129341, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:37\n",
      "2021-08-12 19:45:21 [INFO]\t[TRAIN] Epoch=7/20, Step=8/22, loss=0.189755, acc1=0.90625, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:36\n",
      "2021-08-12 19:45:21 [INFO]\t[TRAIN] Epoch=7/20, Step=10/22, loss=0.182541, acc1=0.90625, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:36\n",
      "2021-08-12 19:45:21 [INFO]\t[TRAIN] Epoch=7/20, Step=12/22, loss=0.20558, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:35\n",
      "2021-08-12 19:45:22 [INFO]\t[TRAIN] Epoch=7/20, Step=14/22, loss=0.124684, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:34\n",
      "2021-08-12 19:45:22 [INFO]\t[TRAIN] Epoch=7/20, Step=16/22, loss=0.106152, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.33s, eta=0:2:34\n",
      "2021-08-12 19:45:22 [INFO]\t[TRAIN] Epoch=7/20, Step=18/22, loss=0.176706, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.32s, eta=0:2:33\n",
      "2021-08-12 19:45:23 [INFO]\t[TRAIN] Epoch=7/20, Step=20/22, loss=0.124469, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.32s, eta=0:2:32\n",
      "2021-08-12 19:45:23 [INFO]\t[TRAIN] Epoch=7/20, Step=22/22, loss=0.153558, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.16s, eta=0:2:32\n",
      "2021-08-12 19:45:23 [INFO]\t[TRAIN] Epoch 7 finished, loss=0.150589, acc1=0.93608, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:45:23 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:45:28 [INFO]\t[EVAL] Finished, Epoch=7, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:45:28 [INFO]\tModel saved in output/result/epoch_7.\n",
      "2021-08-12 19:45:28 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:45:36 [INFO]\t[TRAIN] Epoch=8/20, Step=2/22, loss=0.093645, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:2:37\n",
      "2021-08-12 19:45:36 [INFO]\t[TRAIN] Epoch=8/20, Step=4/22, loss=0.122441, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.53s, eta=0:2:36\n",
      "2021-08-12 19:45:36 [INFO]\t[TRAIN] Epoch=8/20, Step=6/22, loss=0.038866, acc1=1.0, acc3=1.0, lr=0.0003, time_each_step=0.53s, eta=0:2:35\n",
      "2021-08-12 19:45:37 [INFO]\t[TRAIN] Epoch=8/20, Step=8/22, loss=0.058722, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:2:34\n",
      "2021-08-12 19:45:37 [INFO]\t[TRAIN] Epoch=8/20, Step=10/22, loss=0.113022, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:2:33\n",
      "2021-08-12 19:45:37 [INFO]\t[TRAIN] Epoch=8/20, Step=12/22, loss=0.13362, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:2:31\n",
      "2021-08-12 19:45:38 [INFO]\t[TRAIN] Epoch=8/20, Step=14/22, loss=0.184274, acc1=0.90625, acc3=1.0, lr=0.0003, time_each_step=0.53s, eta=0:2:30\n",
      "2021-08-12 19:45:38 [INFO]\t[TRAIN] Epoch=8/20, Step=16/22, loss=0.08465, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.53s, eta=0:2:29\n",
      "2021-08-12 19:45:38 [INFO]\t[TRAIN] Epoch=8/20, Step=18/22, loss=0.142983, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.53s, eta=0:2:28\n",
      "2021-08-12 19:45:39 [INFO]\t[TRAIN] Epoch=8/20, Step=20/22, loss=0.076172, acc1=1.0, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:2:27\n",
      "2021-08-12 19:45:39 [INFO]\t[TRAIN] Epoch=8/20, Step=22/22, loss=0.092531, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.16s, eta=0:2:26\n",
      "2021-08-12 19:45:39 [INFO]\t[TRAIN] Epoch 8 finished, loss=0.124488, acc1=0.953125, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:45:39 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  2.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:45:42 [INFO]\t[EVAL] Finished, Epoch=8, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:45:43 [INFO]\tModel saved in output/result/epoch_8.\n",
      "2021-08-12 19:45:43 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:45:45 [INFO]\t[TRAIN] Epoch=9/20, Step=2/22, loss=0.193987, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:50\n",
      "2021-08-12 19:45:45 [INFO]\t[TRAIN] Epoch=9/20, Step=4/22, loss=0.11838, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:49\n",
      "2021-08-12 19:45:46 [INFO]\t[TRAIN] Epoch=9/20, Step=6/22, loss=0.120688, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:49\n",
      "2021-08-12 19:45:46 [INFO]\t[TRAIN] Epoch=9/20, Step=8/22, loss=0.154478, acc1=0.90625, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:48\n",
      "2021-08-12 19:45:46 [INFO]\t[TRAIN] Epoch=9/20, Step=10/22, loss=0.063285, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:48\n",
      "2021-08-12 19:45:47 [INFO]\t[TRAIN] Epoch=9/20, Step=12/22, loss=0.100501, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:47\n",
      "2021-08-12 19:45:47 [INFO]\t[TRAIN] Epoch=9/20, Step=14/22, loss=0.157138, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:47\n",
      "2021-08-12 19:45:47 [INFO]\t[TRAIN] Epoch=9/20, Step=16/22, loss=0.106021, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.26s, eta=0:2:46\n",
      "2021-08-12 19:45:47 [INFO]\t[TRAIN] Epoch=9/20, Step=18/22, loss=0.13536, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.25s, eta=0:2:46\n",
      "2021-08-12 19:45:48 [INFO]\t[TRAIN] Epoch=9/20, Step=20/22, loss=0.138035, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.25s, eta=0:2:45\n",
      "2021-08-12 19:45:48 [INFO]\t[TRAIN] Epoch=9/20, Step=22/22, loss=0.112449, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.15s, eta=0:2:45\n",
      "2021-08-12 19:45:48 [INFO]\t[TRAIN] Epoch 9 finished, loss=0.127929, acc1=0.949574, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:45:48 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:45:52 [INFO]\t[EVAL] Finished, Epoch=9, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:45:52 [INFO]\tModel saved in output/result/epoch_9.\n",
      "2021-08-12 19:45:52 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:46:00 [INFO]\t[TRAIN] Epoch=10/20, Step=2/22, loss=0.120612, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.51s, eta=0:1:51\n",
      "2021-08-12 19:46:00 [INFO]\t[TRAIN] Epoch=10/20, Step=4/22, loss=0.063667, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:50\n",
      "2021-08-12 19:46:01 [INFO]\t[TRAIN] Epoch=10/20, Step=6/22, loss=0.238539, acc1=0.890625, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:49\n",
      "2021-08-12 19:46:01 [INFO]\t[TRAIN] Epoch=10/20, Step=8/22, loss=0.068072, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:48\n",
      "2021-08-12 19:46:01 [INFO]\t[TRAIN] Epoch=10/20, Step=10/22, loss=0.056509, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:47\n",
      "2021-08-12 19:46:02 [INFO]\t[TRAIN] Epoch=10/20, Step=12/22, loss=0.156749, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:45\n",
      "2021-08-12 19:46:02 [INFO]\t[TRAIN] Epoch=10/20, Step=14/22, loss=0.119919, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:44\n",
      "2021-08-12 19:46:02 [INFO]\t[TRAIN] Epoch=10/20, Step=16/22, loss=0.148596, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:43\n",
      "2021-08-12 19:46:03 [INFO]\t[TRAIN] Epoch=10/20, Step=18/22, loss=0.134328, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:42\n",
      "2021-08-12 19:46:03 [INFO]\t[TRAIN] Epoch=10/20, Step=20/22, loss=0.118619, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.52s, eta=0:1:41\n",
      "2021-08-12 19:46:03 [INFO]\t[TRAIN] Epoch=10/20, Step=22/22, loss=0.133241, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.16s, eta=0:1:40\n",
      "2021-08-12 19:46:03 [INFO]\t[TRAIN] Epoch 10 finished, loss=0.13972, acc1=0.942472, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:46:03 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:06<00:00,  1.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:46:10 [INFO]\t[EVAL] Finished, Epoch=10, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:46:10 [INFO]\tModel saved in output/result/epoch_10.\n",
      "2021-08-12 19:46:10 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:46:13 [INFO]\t[TRAIN] Epoch=11/20, Step=2/22, loss=0.165459, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.29s, eta=0:2:53\n",
      "2021-08-12 19:46:14 [INFO]\t[TRAIN] Epoch=11/20, Step=4/22, loss=0.066068, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.29s, eta=0:2:53\n",
      "2021-08-12 19:46:14 [INFO]\t[TRAIN] Epoch=11/20, Step=6/22, loss=0.074487, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.3s, eta=0:2:52\n",
      "2021-08-12 19:46:14 [INFO]\t[TRAIN] Epoch=11/20, Step=8/22, loss=0.095005, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.3s, eta=0:2:52\n",
      "2021-08-12 19:46:15 [INFO]\t[TRAIN] Epoch=11/20, Step=10/22, loss=0.152596, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.3s, eta=0:2:51\n",
      "2021-08-12 19:46:15 [INFO]\t[TRAIN] Epoch=11/20, Step=12/22, loss=0.097289, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.3s, eta=0:2:51\n",
      "2021-08-12 19:46:16 [INFO]\t[TRAIN] Epoch=11/20, Step=14/22, loss=0.246864, acc1=0.875, acc3=1.0, lr=0.0003, time_each_step=0.31s, eta=0:2:50\n",
      "2021-08-12 19:46:16 [INFO]\t[TRAIN] Epoch=11/20, Step=16/22, loss=0.107669, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.31s, eta=0:2:50\n",
      "2021-08-12 19:46:16 [INFO]\t[TRAIN] Epoch=11/20, Step=18/22, loss=0.085167, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.31s, eta=0:2:49\n",
      "2021-08-12 19:46:17 [INFO]\t[TRAIN] Epoch=11/20, Step=20/22, loss=0.133144, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.31s, eta=0:2:48\n",
      "2021-08-12 19:46:17 [INFO]\t[TRAIN] Epoch=11/20, Step=22/22, loss=0.064338, acc1=1.0, acc3=1.0, lr=0.0003, time_each_step=0.18s, eta=0:2:48\n",
      "2021-08-12 19:46:17 [INFO]\t[TRAIN] Epoch 11 finished, loss=0.126478, acc1=0.953125, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:46:17 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:05<00:00,  1.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:46:23 [INFO]\t[EVAL] Finished, Epoch=11, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:46:23 [INFO]\tModel saved in output/result/epoch_11.\n",
      "2021-08-12 19:46:23 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:46:29 [INFO]\t[TRAIN] Epoch=12/20, Step=2/22, loss=0.104558, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.42s, eta=0:1:59\n",
      "2021-08-12 19:46:29 [INFO]\t[TRAIN] Epoch=12/20, Step=4/22, loss=0.083746, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.42s, eta=0:1:58\n",
      "2021-08-12 19:46:29 [INFO]\t[TRAIN] Epoch=12/20, Step=6/22, loss=0.131435, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.42s, eta=0:1:57\n",
      "2021-08-12 19:46:30 [INFO]\t[TRAIN] Epoch=12/20, Step=8/22, loss=0.111411, acc1=0.96875, acc3=1.0, lr=0.0003, time_each_step=0.42s, eta=0:1:56\n",
      "2021-08-12 19:46:30 [INFO]\t[TRAIN] Epoch=12/20, Step=10/22, loss=0.157212, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.42s, eta=0:1:55\n",
      "2021-08-12 19:46:30 [INFO]\t[TRAIN] Epoch=12/20, Step=12/22, loss=0.179315, acc1=0.9375, acc3=1.0, lr=0.0003, time_each_step=0.41s, eta=0:1:54\n",
      "2021-08-12 19:46:31 [INFO]\t[TRAIN] Epoch=12/20, Step=14/22, loss=0.062889, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.41s, eta=0:1:53\n",
      "2021-08-12 19:46:31 [INFO]\t[TRAIN] Epoch=12/20, Step=16/22, loss=0.143241, acc1=0.953125, acc3=1.0, lr=0.0003, time_each_step=0.41s, eta=0:1:53\n",
      "2021-08-12 19:46:31 [INFO]\t[TRAIN] Epoch=12/20, Step=18/22, loss=0.197136, acc1=0.90625, acc3=1.0, lr=0.0003, time_each_step=0.41s, eta=0:1:52\n",
      "2021-08-12 19:46:31 [INFO]\t[TRAIN] Epoch=12/20, Step=20/22, loss=0.185505, acc1=0.921875, acc3=1.0, lr=0.0003, time_each_step=0.4s, eta=0:1:51\n",
      "2021-08-12 19:46:32 [INFO]\t[TRAIN] Epoch=12/20, Step=22/22, loss=0.07255, acc1=0.984375, acc3=1.0, lr=0.0003, time_each_step=0.16s, eta=0:1:50\n",
      "2021-08-12 19:46:32 [INFO]\t[TRAIN] Epoch 12 finished, loss=0.127536, acc1=0.954545, acc3=1.0, lr=0.0003 .\n",
      "2021-08-12 19:46:32 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:05<00:00,  1.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:46:38 [INFO]\t[EVAL] Finished, Epoch=12, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:46:38 [INFO]\tModel saved in output/result/epoch_12.\n",
      "2021-08-12 19:46:38 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:46:40 [INFO]\t[TRAIN] Epoch=13/20, Step=2/22, loss=0.080469, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.24s, eta=0:1:52\n",
      "2021-08-12 19:46:40 [INFO]\t[TRAIN] Epoch=13/20, Step=4/22, loss=0.064333, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.24s, eta=0:1:51\n",
      "2021-08-12 19:46:41 [INFO]\t[TRAIN] Epoch=13/20, Step=6/22, loss=0.095851, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.26s, eta=0:1:51\n",
      "2021-08-12 19:46:41 [INFO]\t[TRAIN] Epoch=13/20, Step=8/22, loss=0.084402, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:50\n",
      "2021-08-12 19:46:41 [INFO]\t[TRAIN] Epoch=13/20, Step=10/22, loss=0.24471, acc1=0.90625, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:50\n",
      "2021-08-12 19:46:42 [INFO]\t[TRAIN] Epoch=13/20, Step=12/22, loss=0.131819, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:49\n",
      "2021-08-12 19:46:42 [INFO]\t[TRAIN] Epoch=13/20, Step=14/22, loss=0.088453, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:49\n",
      "2021-08-12 19:46:42 [INFO]\t[TRAIN] Epoch=13/20, Step=16/22, loss=0.176348, acc1=0.90625, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:48\n",
      "2021-08-12 19:46:43 [INFO]\t[TRAIN] Epoch=13/20, Step=18/22, loss=0.179459, acc1=0.9375, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:48\n",
      "2021-08-12 19:46:43 [INFO]\t[TRAIN] Epoch=13/20, Step=20/22, loss=0.065527, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.25s, eta=0:1:47\n",
      "2021-08-12 19:46:43 [INFO]\t[TRAIN] Epoch=13/20, Step=22/22, loss=0.066165, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.17s, eta=0:1:47\n",
      "2021-08-12 19:46:43 [INFO]\t[TRAIN] Epoch 13 finished, loss=0.140521, acc1=0.944602, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:46:43 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:46:48 [INFO]\t[EVAL] Finished, Epoch=13, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:46:48 [INFO]\tModel saved in output/result/epoch_13.\n",
      "2021-08-12 19:46:48 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:46:52 [INFO]\t[TRAIN] Epoch=14/20, Step=2/22, loss=0.182837, acc1=0.9375, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:14\n",
      "2021-08-12 19:46:52 [INFO]\t[TRAIN] Epoch=14/20, Step=4/22, loss=0.063522, acc1=1.0, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:13\n",
      "2021-08-12 19:46:52 [INFO]\t[TRAIN] Epoch=14/20, Step=6/22, loss=0.159413, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:13\n",
      "2021-08-12 19:46:53 [INFO]\t[TRAIN] Epoch=14/20, Step=8/22, loss=0.138616, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:12\n",
      "2021-08-12 19:46:53 [INFO]\t[TRAIN] Epoch=14/20, Step=10/22, loss=0.093026, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.32s, eta=0:1:12\n",
      "2021-08-12 19:46:53 [INFO]\t[TRAIN] Epoch=14/20, Step=12/22, loss=0.097761, acc1=1.0, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:11\n",
      "2021-08-12 19:46:54 [INFO]\t[TRAIN] Epoch=14/20, Step=14/22, loss=0.120695, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:10\n",
      "2021-08-12 19:46:54 [INFO]\t[TRAIN] Epoch=14/20, Step=16/22, loss=0.05886, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:10\n",
      "2021-08-12 19:46:54 [INFO]\t[TRAIN] Epoch=14/20, Step=18/22, loss=0.177017, acc1=0.921875, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:9\n",
      "2021-08-12 19:46:55 [INFO]\t[TRAIN] Epoch=14/20, Step=20/22, loss=0.22716, acc1=0.921875, acc3=1.0, lr=3e-05, time_each_step=0.31s, eta=0:1:8\n",
      "2021-08-12 19:46:55 [INFO]\t[TRAIN] Epoch=14/20, Step=22/22, loss=0.120044, acc1=0.9375, acc3=1.0, lr=3e-05, time_each_step=0.15s, eta=0:1:8\n",
      "2021-08-12 19:46:55 [INFO]\t[TRAIN] Epoch 14 finished, loss=0.130645, acc1=0.951705, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:46:55 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:05<00:00,  1.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:01 [INFO]\t[EVAL] Finished, Epoch=14, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:01 [INFO]\tModel saved in output/result/epoch_14.\n",
      "2021-08-12 19:47:01 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:47:05 [INFO]\t[TRAIN] Epoch=15/20, Step=2/22, loss=0.050474, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:16\n",
      "2021-08-12 19:47:05 [INFO]\t[TRAIN] Epoch=15/20, Step=4/22, loss=0.117057, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:15\n",
      "2021-08-12 19:47:06 [INFO]\t[TRAIN] Epoch=15/20, Step=6/22, loss=0.173755, acc1=0.90625, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:15\n",
      "2021-08-12 19:47:06 [INFO]\t[TRAIN] Epoch=15/20, Step=8/22, loss=0.106663, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:14\n",
      "2021-08-12 19:47:06 [INFO]\t[TRAIN] Epoch=15/20, Step=10/22, loss=0.106757, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.33s, eta=0:1:13\n",
      "2021-08-12 19:47:07 [INFO]\t[TRAIN] Epoch=15/20, Step=12/22, loss=0.118249, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:1:13\n",
      "2021-08-12 19:47:07 [INFO]\t[TRAIN] Epoch=15/20, Step=14/22, loss=0.112334, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:1:12\n",
      "2021-08-12 19:47:08 [INFO]\t[TRAIN] Epoch=15/20, Step=16/22, loss=0.205397, acc1=0.90625, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:1:12\n",
      "2021-08-12 19:47:08 [INFO]\t[TRAIN] Epoch=15/20, Step=18/22, loss=0.113302, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:1:11\n",
      "2021-08-12 19:47:08 [INFO]\t[TRAIN] Epoch=15/20, Step=20/22, loss=0.050981, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:1:10\n",
      "2021-08-12 19:47:09 [INFO]\t[TRAIN] Epoch=15/20, Step=22/22, loss=0.08584, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.19s, eta=0:1:9\n",
      "2021-08-12 19:47:09 [INFO]\t[TRAIN] Epoch 15 finished, loss=0.105718, acc1=0.963068, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:47:09 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:13 [INFO]\t[EVAL] Finished, Epoch=15, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:14 [INFO]\tModel saved in output/result/epoch_15.\n",
      "2021-08-12 19:47:14 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:47:18 [INFO]\t[TRAIN] Epoch=16/20, Step=2/22, loss=0.110879, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:1:2\n",
      "2021-08-12 19:47:18 [INFO]\t[TRAIN] Epoch=16/20, Step=4/22, loss=0.109799, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.37s, eta=0:1:1\n",
      "2021-08-12 19:47:18 [INFO]\t[TRAIN] Epoch=16/20, Step=6/22, loss=0.087542, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.37s, eta=0:1:0\n",
      "2021-08-12 19:47:19 [INFO]\t[TRAIN] Epoch=16/20, Step=8/22, loss=0.078821, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.38s, eta=0:1:0\n",
      "2021-08-12 19:47:19 [INFO]\t[TRAIN] Epoch=16/20, Step=10/22, loss=0.081417, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:0:59\n",
      "2021-08-12 19:47:19 [INFO]\t[TRAIN] Epoch=16/20, Step=12/22, loss=0.143467, acc1=0.9375, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:0:58\n",
      "2021-08-12 19:47:20 [INFO]\t[TRAIN] Epoch=16/20, Step=14/22, loss=0.133208, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:0:57\n",
      "2021-08-12 19:47:20 [INFO]\t[TRAIN] Epoch=16/20, Step=16/22, loss=0.120237, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:0:56\n",
      "2021-08-12 19:47:20 [INFO]\t[TRAIN] Epoch=16/20, Step=18/22, loss=0.156168, acc1=0.921875, acc3=1.0, lr=3e-05, time_each_step=0.36s, eta=0:0:56\n",
      "2021-08-12 19:47:21 [INFO]\t[TRAIN] Epoch=16/20, Step=20/22, loss=0.048246, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.35s, eta=0:0:55\n",
      "2021-08-12 19:47:21 [INFO]\t[TRAIN] Epoch=16/20, Step=22/22, loss=0.100101, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.17s, eta=0:0:54\n",
      "2021-08-12 19:47:21 [INFO]\t[TRAIN] Epoch 16 finished, loss=0.105063, acc1=0.962358, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:47:21 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:26 [INFO]\t[EVAL] Finished, Epoch=16, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:26 [INFO]\tModel saved in output/result/epoch_16.\n",
      "2021-08-12 19:47:26 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:47:28 [INFO]\t[TRAIN] Epoch=17/20, Step=2/22, loss=0.08049, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:47\n",
      "2021-08-12 19:47:29 [INFO]\t[TRAIN] Epoch=17/20, Step=4/22, loss=0.074838, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:46\n",
      "2021-08-12 19:47:29 [INFO]\t[TRAIN] Epoch=17/20, Step=6/22, loss=0.119387, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:46\n",
      "2021-08-12 19:47:29 [INFO]\t[TRAIN] Epoch=17/20, Step=8/22, loss=0.073784, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:45\n",
      "2021-08-12 19:47:30 [INFO]\t[TRAIN] Epoch=17/20, Step=10/22, loss=0.158116, acc1=0.9375, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:45\n",
      "2021-08-12 19:47:30 [INFO]\t[TRAIN] Epoch=17/20, Step=12/22, loss=0.208539, acc1=0.859375, acc3=1.0, lr=3e-05, time_each_step=0.26s, eta=0:0:44\n",
      "2021-08-12 19:47:30 [INFO]\t[TRAIN] Epoch=17/20, Step=14/22, loss=0.085579, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.27s, eta=0:0:43\n",
      "2021-08-12 19:47:31 [INFO]\t[TRAIN] Epoch=17/20, Step=16/22, loss=0.12829, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.26s, eta=0:0:43\n",
      "2021-08-12 19:47:31 [INFO]\t[TRAIN] Epoch=17/20, Step=18/22, loss=0.094663, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.26s, eta=0:0:42\n",
      "2021-08-12 19:47:31 [INFO]\t[TRAIN] Epoch=17/20, Step=20/22, loss=0.084901, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.26s, eta=0:0:42\n",
      "2021-08-12 19:47:32 [INFO]\t[TRAIN] Epoch=17/20, Step=22/22, loss=0.14935, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.16s, eta=0:0:41\n",
      "2021-08-12 19:47:32 [INFO]\t[TRAIN] Epoch 17 finished, loss=0.123044, acc1=0.954545, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:47:32 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  2.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:35 [INFO]\t[EVAL] Finished, Epoch=17, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:35 [INFO]\tModel saved in output/result/epoch_17.\n",
      "2021-08-12 19:47:35 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:47:38 [INFO]\t[TRAIN] Epoch=18/20, Step=2/22, loss=0.145868, acc1=0.921875, acc3=1.0, lr=3e-05, time_each_step=0.28s, eta=0:0:28\n",
      "2021-08-12 19:47:39 [INFO]\t[TRAIN] Epoch=18/20, Step=4/22, loss=0.066338, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:27\n",
      "2021-08-12 19:47:39 [INFO]\t[TRAIN] Epoch=18/20, Step=6/22, loss=0.065236, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.28s, eta=0:0:27\n",
      "2021-08-12 19:47:39 [INFO]\t[TRAIN] Epoch=18/20, Step=8/22, loss=0.067585, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:26\n",
      "2021-08-12 19:47:40 [INFO]\t[TRAIN] Epoch=18/20, Step=10/22, loss=0.047192, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:26\n",
      "2021-08-12 19:47:40 [INFO]\t[TRAIN] Epoch=18/20, Step=12/22, loss=0.111684, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:25\n",
      "2021-08-12 19:47:40 [INFO]\t[TRAIN] Epoch=18/20, Step=14/22, loss=0.066736, acc1=0.984375, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:25\n",
      "2021-08-12 19:47:41 [INFO]\t[TRAIN] Epoch=18/20, Step=16/22, loss=0.15815, acc1=0.921875, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:24\n",
      "2021-08-12 19:47:41 [INFO]\t[TRAIN] Epoch=18/20, Step=18/22, loss=0.106744, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.29s, eta=0:0:23\n",
      "2021-08-12 19:47:41 [INFO]\t[TRAIN] Epoch=18/20, Step=20/22, loss=0.092599, acc1=0.96875, acc3=1.0, lr=3e-05, time_each_step=0.28s, eta=0:0:23\n",
      "2021-08-12 19:47:41 [INFO]\t[TRAIN] Epoch=18/20, Step=22/22, loss=0.129186, acc1=0.953125, acc3=1.0, lr=3e-05, time_each_step=0.15s, eta=0:0:22\n",
      "2021-08-12 19:47:41 [INFO]\t[TRAIN] Epoch 18 finished, loss=0.121921, acc1=0.950284, acc3=1.0, lr=3e-05 .\n",
      "2021-08-12 19:47:41 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  1.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:45 [INFO]\t[EVAL] Finished, Epoch=18, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:46 [INFO]\tModel saved in output/result/epoch_18.\n",
      "2021-08-12 19:47:46 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:47:48 [INFO]\t[TRAIN] Epoch=19/20, Step=2/22, loss=0.19997, acc1=0.921875, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:19\n",
      "2021-08-12 19:47:48 [INFO]\t[TRAIN] Epoch=19/20, Step=4/22, loss=0.12854, acc1=0.953125, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:18\n",
      "2021-08-12 19:47:49 [INFO]\t[TRAIN] Epoch=19/20, Step=6/22, loss=0.055148, acc1=0.984375, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:18\n",
      "2021-08-12 19:47:49 [INFO]\t[TRAIN] Epoch=19/20, Step=8/22, loss=0.099786, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:17\n",
      "2021-08-12 19:47:49 [INFO]\t[TRAIN] Epoch=19/20, Step=10/22, loss=0.045039, acc1=1.0, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:17\n",
      "2021-08-12 19:47:50 [INFO]\t[TRAIN] Epoch=19/20, Step=12/22, loss=0.141071, acc1=0.921875, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:16\n",
      "2021-08-12 19:47:50 [INFO]\t[TRAIN] Epoch=19/20, Step=14/22, loss=0.109613, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:16\n",
      "2021-08-12 19:47:50 [INFO]\t[TRAIN] Epoch=19/20, Step=16/22, loss=0.155989, acc1=0.953125, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:15\n",
      "2021-08-12 19:47:51 [INFO]\t[TRAIN] Epoch=19/20, Step=18/22, loss=0.159907, acc1=0.921875, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:15\n",
      "2021-08-12 19:47:51 [INFO]\t[TRAIN] Epoch=19/20, Step=20/22, loss=0.160907, acc1=0.9375, acc3=1.0, lr=3e-06, time_each_step=0.26s, eta=0:0:14\n",
      "2021-08-12 19:47:51 [INFO]\t[TRAIN] Epoch=19/20, Step=22/22, loss=0.072452, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.16s, eta=0:0:14\n",
      "2021-08-12 19:47:51 [INFO]\t[TRAIN] Epoch 19 finished, loss=0.114667, acc1=0.958097, acc3=1.0, lr=3e-06 .\n",
      "2021-08-12 19:47:51 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:06<00:00,  1.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:47:58 [INFO]\t[EVAL] Finished, Epoch=19, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:47:58 [INFO]\tModel saved in output/result/epoch_19.\n",
      "2021-08-12 19:47:58 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n",
      "2021-08-12 19:48:01 [INFO]\t[TRAIN] Epoch=20/20, Step=2/22, loss=0.132629, acc1=0.953125, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:12\n",
      "2021-08-12 19:48:02 [INFO]\t[TRAIN] Epoch=20/20, Step=4/22, loss=0.094429, acc1=0.984375, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:12\n",
      "2021-08-12 19:48:02 [INFO]\t[TRAIN] Epoch=20/20, Step=6/22, loss=0.079247, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:11\n",
      "2021-08-12 19:48:02 [INFO]\t[TRAIN] Epoch=20/20, Step=8/22, loss=0.052849, acc1=0.984375, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:10\n",
      "2021-08-12 19:48:03 [INFO]\t[TRAIN] Epoch=20/20, Step=10/22, loss=0.315649, acc1=0.875, acc3=1.0, lr=3e-06, time_each_step=0.31s, eta=0:0:10\n",
      "2021-08-12 19:48:03 [INFO]\t[TRAIN] Epoch=20/20, Step=12/22, loss=0.191254, acc1=0.90625, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:9\n",
      "2021-08-12 19:48:03 [INFO]\t[TRAIN] Epoch=20/20, Step=14/22, loss=0.08337, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:9\n",
      "2021-08-12 19:48:04 [INFO]\t[TRAIN] Epoch=20/20, Step=16/22, loss=0.096715, acc1=0.984375, acc3=1.0, lr=3e-06, time_each_step=0.3s, eta=0:0:8\n",
      "2021-08-12 19:48:04 [INFO]\t[TRAIN] Epoch=20/20, Step=18/22, loss=0.107312, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.32s, eta=0:0:8\n",
      "2021-08-12 19:48:05 [INFO]\t[TRAIN] Epoch=20/20, Step=20/22, loss=0.119108, acc1=0.96875, acc3=1.0, lr=3e-06, time_each_step=0.32s, eta=0:0:7\n",
      "2021-08-12 19:48:05 [INFO]\t[TRAIN] Epoch=20/20, Step=22/22, loss=0.124179, acc1=0.9375, acc3=1.0, lr=3e-06, time_each_step=0.18s, eta=0:0:6\n",
      "2021-08-12 19:48:05 [INFO]\t[TRAIN] Epoch 20 finished, loss=0.125353, acc1=0.953835, acc3=1.0, lr=3e-06 .\n",
      "2021-08-12 19:48:05 [INFO]\tStart to evaluating(total_samples=419, total_steps=7)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-12 19:48:10 [INFO]\t[EVAL] Finished, Epoch=20, acc1=1.0, acc3=1.0 .\n",
      "2021-08-12 19:48:10 [INFO]\tModel saved in output/result/epoch_20.\n",
      "2021-08-12 19:48:10 [INFO]\tCurrent evaluated best model in eval_dataset is epoch_5, acc1=1.0\n"
     ]
    }
   ],
   "source": [
    "train_dataset = pdx.datasets.ImageNet(\r\n",
    "    data_dir='hand',\r\n",
    "    file_list='hand/train_list.txt',\r\n",
    "    label_list='hand/labels.txt',\r\n",
    "    transforms=train_transforms,\r\n",
    "    shuffle=True)\r\n",
    "eval_dataset = pdx.datasets.ImageNet(\r\n",
    "    data_dir='hand',\r\n",
    "    file_list='hand/val_list.txt',\r\n",
    "    label_list='hand/labels.txt',\r\n",
    "    transforms=eval_transforms)\r\n",
    "\r\n",
    "model = pdx.cls.ResNet18(num_classes=len(train_dataset.labels))\r\n",
    "\r\n",
    "model.train(\r\n",
    "    num_epochs=20,\r\n",
    "    train_dataset=train_dataset,\r\n",
    "    train_batch_size=64,\r\n",
    "    eval_dataset=eval_dataset,\r\n",
    "    lr_decay_epochs=[6, 12, 18],\r\n",
    "    learning_rate=0.003,\r\n",
    "    save_dir='output/result',\r\n",
    "    use_vdl=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 4.模型评测\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/439d94982e9e47f3b5b3ef3619b9e657c21736376efb4395a2b063448bb40d0d)\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/82588bf305f24ba694e0ab7c04d8b90d36ed0f6dc7714292a1471da9b87dcdd4)\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/cfdd35e2a10841fe8ca1e1841c9a74490d809453ed3b484b93725c18660ededb)\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/1557e1d5bed14c4683be7978ada0dce8b19614a586b54a378856735441c34428)\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/4171b66ea6c14352a31356c2ee5864885c814ea7fec24f808d5a9bbb8cb22f4f)\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/700285acfd3f4e0c9d7151588bc05a516af10a95abef43959f64a0de550c9030)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 5.模型预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 5.1批量预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 21:10:06 [INFO]\tModel[ResNet18] loaded.\n"
     ]
    },
    {
     "data": {
      "image/png": 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R2yAw9frws/53So1Wz5Nuaam/V+WepVMYTKobbQx9Wa3Ztm7Wa7lxvX2z+hCeG5vwShOLEzgRPzbu+8nawGWcPmCAG7mJjgQDUapKK6Kv0MtSlI9FPPILuLa7RjSCILmYKgvSV6gIG/BKT0O+lDPivCZTKnoPpe1IHZntlar5tVlUggw6i9rfpqhsHP1I4PxY1feJMTZCUfv95ImZdpdlIUyzXT8cmKaJFJXloOgucPfikqhmBWqm4jJeg4+B5xbKvFdL0qDgdAio6nbotencl31eGETcU51G5zRtjTSx8VltbhCH1t6KAJ70AwYC0eo8ZRN7XnglicWJTFfEjlHUaKU3dveulNxEdrt5Kg82zqJ8D2IKT9UNB5zio+C4gsYhZJNjJSs5H1FVc8fW2dpJGWJAkiknF0nMCUJSkiQSiWNOsFg9iUoqyJ2ygphWwhSMnVUdYkJSJk6m2MqFCIQQIIuZJoNYP+qOWxAoq5KXTjCqKXQKgmQTdWI0749JhJwTghJRcjF3TtNESond2Z705AnLsvD44UOblxg4pvfZ7XZGPFLiCDy8OrJcKVEXvuvTb/N3H3yLkBckdmep6QgxKTpn46qiXy9GBW0KHCHI2QhEnb4yx7LeME7Wx2pNOXE40JGw1dkiPq2pbSJxokZoiI3pavzvcr9Zl8x0s+pfWfcUWUoVQig+Jh9dEnlliMW4a3dYy8PloqOqL9j+80LdrSqr95xG8LprVS9C/9xAQVTXJ1MIFgViddmunpNU5KUpJAPC8XgcxAAV7QvYWm4Lq717XWQKIYaC2E6UcMTB922apiKWdJ8Ob0HxIkrdtUOIrV41F5vuZSnihHB9fc1+vycfDpBhWRILSs4LX3ztU1wdrvnG4yuzGCGkYzLLUsqEuVoHikV8vXvWzx6gMczflpgyrL+t3bvOL5woVz8WuKE/mxaQRrSqyei0/y+jx68MsXhuWOkVngVbLPNTy1cWTk+13ENbQivn7/duajGYWFyCALEgZZTQPDNzNvOsWUX6NRXbuZei4ESszBS7HqGzuT3StDwdnK5hkoBMVRQRcvHT8LoJ71tR+9DiUETIqRMGL4p4vYcfo+qhut/viTGy2+149OgRxyLmEIQQbJzSEXQKHEU55IU7uzPeuHMPHn+TpEqUatoNTFM0HQxFZZu1uVEPgV2G1WOwVp+c2tntRbC1Xp4T6V50vW2JGy8MHiecaKbrMh8RXhli0Z1TKoPolUe5FrLPrCd1PXRu4GaOpV1ru1AoFDobe17rSpU328PGJrxZz7WrCqLaQtEDsA8T+zARJZQGDXFD0T2QzLkno2ZkKT4K5MLIqpJTd77yO2T0u6VUy4eJMSGGRhyq/0VKR3KujlbKko7lnrVjXMUO1cThcDTvT2fCBQY/jBCMAGaTGAkhcufinGWZOR6PvPnmmyzLwqN33mk+HtUcqwLpWkmTEKZIvjrw6fN7fO7sLu88fshBEmEXaV6bSzbXG4SkFrsCo8KwfYfG5d2kU6CWeR6cfRaheQpsEpJ1O567q/dr30N3daeXsGaqN2mJnTKRy+n4PiI9gleGWCjmqm2/ytQ7Nt5NUFEa3mSlqMoud7PVO2FTNyYqAOoI19COr/MUSm3m0u6EFcU8IGeJTE3U0ObSLSLEtqBtAMzzMzcOpT4v59zMoUtVokrnLDzhDL4tp+SEfOLKXU27lctYm0bVtZGdErT+NaJB9xKt3qqHw4HdbuLOnQsePnzIoydPAItt0dm4kDmpWa0pY5WVz+/vMS3Ku9ePeVhEkCkLy2QRubk8W/LqvaXHuphK64a58nNoDY5ItbE+mkijN3AtN0HZ/WWl7K5zMBatay2MupAmbnGC/CfK99AV3Hb9+bt6E7wixGKEujOcyJFtELPDWVdGS9qUsCYMjgoNYoz7XiJcG9PSWPwKMkYNDh0eZf0gQowB0kIUIxKSlbv7M+7szhBVRI1jqS7Uy7IYx1FFAgoSpzzsisETjRCcF2AhAEIP+tLQYkTWbuY+fqO+aPXpABoHUvUOVayRIhrFaK7Q8zy3917rPOpzdrsddy/vmI+Iwt/4f/8mcxSurjN79sh+LoFywkGPHGYlpczn7r/G/f2erz18j79x9W1yhOugoIUoiRT9C21MrA+FwAUX65Idd1rH04uNeUMLWNeKW3/+oHjDxZs3rgFkZYHxxK2M9da62ipP21S21rrrXA20fBmiDq9wwt5qBquWkOFvKOgm/mky6JoN3eIyNuoPPhD5dCFIrVt/K+iSOpeSitOUKvt5x26a0CL/h6l7RHodgJ3vk0cZXDuncdNOVM2chjAufN0pIetfLdf67frguYbaZhU5cv0sfal+Hp5QTFUnk0ykkzJuu92OeZ6JMXJ1dYVkJR2OXF9fm2iEktQ4jThNTBLYh4nXdxecEyFZmaCG3IMFyhE9KIrPp+HHeq7bb8fJ+p8KImEYQ/xcbCHsc4Ifu42b1j3fti+bC+HYfK7tuhJeDpq/UpzFKXuWTU6rs14Xxos0msc2AQs39sTDEZuBa9uaxA0WcORcu2IpSiCrhavvdhN3zs5NZ6GjF2RffKbnUNHGRquqQ4xxnJpJTiClvjNFAcmBRI/z8KLI1k6Y0mjtWBOYqZjyfABb5S4q15EKx1Hb8M+KCBf7M67Pz7l7eYcHDx7Y9OTMdCyLOisxCBGz1iwIF2Fmd3aH1x+dkY+ZJStJFHFcoUWtrwhHHas+MX2O6r0TeuHH5nSib3SEWrW/idR1olqZ03UsLsJ4DZvXn8YxlHvee/ejwitDLFQ4tX1DFyNqpKFHcq+L8IOZ++7od0mrUrImbXEotR+1KRHzmVgj2bDju925LIAgQj4u5ART0V/cu7zDlz73Niyw5CNzMNHj7OysB4epQnHlTjmjdfdXRYqjFoBQA7rG92rvJz3tXUVeHyCmQZmku3WrakOc6jgFzhGsmE79tcPh0JSm3g8mpdRiWeYi+oTCgh+XhXuXd/ji59/m8uycb3z7W+x2OyYVjrtsKgONnKlZRs53eyRm0nHhO+69ye6B8O7D9/lgB0QhKmgyIhoF870AFxMx6q+64rAHx7VyBeoenH31cj9nFwqgWjaycf24h3ECzxAFdN2fFkognIQ9bH3f0Kf19fGCm+wGvDLEAjaopycG/lr7eoMnnrt/otso36Uo4rbYt0q4nsoeWkMn/a+6FmO9CwIrnJ+fM4VoBGLaNV1Ccl6aVXRYs7ee5RXFregen9DGQ5UlLUTM0lEJadU/VEuJV3CWBzkuIw3cBcDiRLDKEXnCMs1ze5+pvFsbk6LbSCkRi0n1/PycfFxICNcXgTPV4r+gzQdkmiZyyGTNXJyd8/rhDuHqyCN9gi6ZKJEUyjhEKRnIDNGrh+roclDMsH4jWc/f6ST3Rm5Yh0+99hwwmuRl+7OWVZdMZ2vDc+K0f78bdSkvAK8UsXg6BHyQ103OWjVoC26a/BX4AXYToI5P9JM5Oj+dUuu6k4qUjFUFqd56403IQmBMFJM0M4VIiJhPzUDUOldkMR512SqW36IgeFCmad/fOUjL19l8I5p1wyliQw/Cq6JOdp/e50JTF2OqKFKDzIBBPBliTuq4FSIBprs4Pz9nv99zOByQJXKZIaiQkhKCMDdmUQjzxLmc8dZZ4u4lfPX6QC6OaUGFhCJqZmiU7oHvEap830Sa50Xy9cb1LLhRHFkV82Wel/vwZTf6ZM562/c+LLwixEJAok1yWpwYgRtwKKgK9bL6lUG3oLiFZn+2Y3kzo5T0bKqBntnIm2VLzJ9UwmHPzfT8mapq/ZbSbzHEnY9CXjLXc0COibfOLnj7/pvEDxbQwPGQIC7sdnsmAlOISNFALIeFmGHKpkfIziSqClmSk8hKPIbEHruBkDWTcup+DKXPYOkWQhCuj9ecnZ2ZmZOeTNcjvees6qdxHfawq6vHTW8RtLh473bFVLobLCUxxqbcvH/vHkGEL7z9Nt/+9rf59gcP+EBhf3HO+fk5BGHa7TgeD0SB8zARIhwvA1dx5rPfesi38iPe0wUOmbM482iGVHNB1KnPp4jVeKVKPFfImZ1zW6tddWeqjlNRU+AW/5y6KMWZnE+Q2e36J4REOCUUTvRYz0Nei+MrW0Vh0ugd/uhKzleEWDByDI3IugVbd4UN6l7FkS0aepPl4EQskaeIHU+hzoY3mZAg1apL5kwiVylzHmbeunOfMxcW35yYYmwmzCoG2K6uJaShE68mEkgXryrxW3uOVmVo1Vd4qLt+fe6YlFqb49ZNY+XbaMTKKWqXZWEqbdd8nvM8c3193XKG7vd7UkrNCsIUeee9d9ntdhyPR87nGVJmFydyLBzD8dj6ff/8ksfHax6TTZRMmbgENNLGZQnGqaxXxSB63MTGr4mI3/XXiL/iCJ7KuWxwsP36abUtaORjq52Tx66C7z4ivFrEwomHdsktVj8HfoD0hgkqbfa5HXUdtd1RMWiLS1y5LZ1IduKNBuftWXaeJMoxK+ca+I43P80/9IV/kHiVURXIlrwlxmgWhpSBEmWaMzkn0jGRkpLTsSNw+ZzmgDhuY83uNyWjIxbrfJle/AgIFA/P5Pwn1qJIHUfVNCTvTcV0GuNAdYaFWolEJXi73Q4R4dGjR9y7d4+Ly0sePnifd7/9Dnfu3OHs7My4JQFZzKfmcn/GMS1MCJ+99zq7EHnvva9yLYrOFjkzJ+Mzr6fCGfZO27qqXEAX5Id1ZOvKIWConKmDjXXkYVPv1j5Ltqy1R/JpI+X5PTP9wChv9OWUU1m5e3+iMmWJFP+XvE0En0FFN+VJEdDFBq6ykb5sFTHExfyfEJ6+o7vOOIpdELJKO4DGwJITr83nfP7Nz3B3f44suWpd2g7pEUpVmwOW/bnMU1RhypLHTNW9WbqVZminEElvDamE0eshGoF0zll5pbvYIkYtZWB5Z80KsUc9Ai083XMgnqsRES4uLtjtdjx58oR79+6R38uW/+L6wLTfMcWZJAo5m8driead48S9s4sSyauEKZgFphACLTv1EFO1xUUMa6WUWXtxPgU0j8cgtHY2wXMPVXQ5FTMAZ9qvFzb6eyPUNitxegksRYFXh1hUKAt9LaNthvM+7XeFpyiBTghQRQhx+o+t+n5hlEmdi+61iiJRAp+6c5/PXr7G2UFZlsrlWIFQ2s85g3STY41LEVyuCZQQYtkYt3c2TwyME7lpOMrzHXfhfRR8ejxvDQkIqi7UWlau+FlRKRxR7mbW2q/9ft84nNrPy8tLjscjh8OBN15/nRAC3/zmN3ny6DGX1TErljD7Yl6eQmS/CMjE3WlPWpSkZjKNGaLaX1aYJXCs8dkVqZ/CvotIT/nfZmEUh7WMRr2tJzv4aj1VEWMLaUPJzNZHsYieNMJ1Ino3zoHVmm68rrvmCPgnTQyp6cOaThLH1jlxoqcKWyl5nkZ9b5Il18SgtLM2SfZmVu0k68OUQVTKTqhcTmf80p//i3h9mdkdMoesaE6A+fsfDgdiRZzYF4ZHVJGueJRgBKPqK5qVZKWz8FCRtVohqkVi7VocFA6roDD/mVvIfcnoXZi0ITw9OU4mdLNqFW2q09Zut+u6ifNzAO7cucPV1ZX5lqjyrXffIWnmjkCcJlRMKVs5icu441wmvuuNz/J33/0m7z15RN7bulAR9tkC90SEY5lTKdyMADmsEFqL2AL0YwBpyK5giksJp2uhrIfafk41Ya9CdQ333IdfbyJISW9o5UYNi3iFUvS5K5xo43FgBVWZDyDET5CfRQglLqfspkI7QQpGTmMMx6ZNBqy4Aeiy6JqwtPvjtUEBpjcoPIfJsnJJbLnlYIcGzfs9IsIhLezDRJJcIgPN52COU4vXMBfqEmHqWM5NgpDTkEplS8nZxoEed+BFjrX44hWVMCbutYA2bZm0vEnZt5Xy0vJ4hqkHo1VOpepMjsdjy3FxdXXVnj3vduyORy4uLuDdd7i+vuYiZ/LxSJwnQojE8r4HMR3Pbpp57eIOx7TwfjxyFGVSm4cwTUW5GYkhNvnfzIm5rwm3MRkFdNm5HYIqnVC0YM5GYGhWszD3g5fUrRUzcYeT9WbXKwGpFhcdnuPX3SDy1HZEIG+vgZs2kg8Drwyx6OdadkQZJrLATRaRTZ8K1b6/fIyfAAAgAElEQVRTCK6xrQEU91nY63a2nS2ElsZfezsi1Ru0I/Ukgc+8/iaiJTWcdqQOJQgK6s7vduecLchQs0Pe3M4QseMc2SQOlRB0vcB2cJInLp5Y5JLcZp1P0+K2tBFOEWnmxbXVpNUrIlbSMTq1cjqHw2Gw1Cg0q0nlPK4O13akQYzEeSrcjY2/GsvFLkTuzWcc9hd8PT1Ag3ENUy6mRaHrhmr/jMcfs6XXs0aqp/DW0nBtdIZ/tTFJOUWsXhs4mPK5EacxzIdvLqdutpe+PitnObZRv3Uf1JdJKOAVIhYwDkAzl7qYh8b2bbFk/n6/cDKZm3BTG5VzKG2dcHuqzAdIKkzYWaTTUfnFb32BX/TZ7+T8emG3BCQn9lHsmNbYcz/UpDhXx0O3cJTsWEFrMFggiukL6qbjs1BVqFmtwPJdppSbn0PTY7i/gVDkDNkS7ngX7qmedQGF+yn9KKEGPozdz1316gwhmM+HLgOHUYnFfr8fdBr1+1tvvcW3v/1t3nnnHeZ55k66y/nlRdPFnEu0VIHM3NtP3GfmnQdPeLAc0VDPPDH9xqNYU83RFJ8SLFO6RWzKsPnUzOnj+R0CU910nM5o9d4nIetezIUy//WWNF+JpveoXHBr71TEaL/Wm0W93/RP3QQOLye71ytFLDahns/5rJd90cG4Qcn11HaEk8jTpGq+AFkgZYLA2299hr1ERAwBcywcxzSdZMmyaFLTkXgEVqUldbHf2TgS6WbN4YQwd11EkCJ+VMLixbSWj7O9cnkm21xLe32RwlhZf9YiULeodHfx2qdUDkqqZlTVRCzjEkSIIbST1O6cX5BeSzz5+hXLsnB9dcV+v4dgruBLiS3MZb6mELmYdlylbLqXCFkEqp6jELYsllgoBGmHM3d/vPIOuX+3z9D9/mQlGuh4pkmJE9gUY0qBldXW1mAj3o15kLF5L3WEVbBZHXNdidAYcazuANvc9IvBK00sao4BL6edDFaFJoOOCqRBXzHoGlb6i6dwJCcyoiuvEhACR7XzPN64vMvbd15nflwOJS7nf8wyEx1yeQSLMXKsJ3dlO1zHziUtR++tXtfX91wD0JydJGvLfenrNMLhxIpmIVFaXIZ9X+VfUCWECSbIC9S8nI2NLh2tcS+VWJheZjSjVqhJhC0HaeBsv0cwX4yrqyuePHnCk8dPeOzElBiKX8U8s+TELMKX7r7JxdVD3j085lsxEfYTuxS4lmy4HiqXZEgpMYw6icZZ9KjZLCMH28WFbcQbLnvOxL2rN3Pb8QxljdPnpOUVneY2/36+T7gI1RInBIMZrIpFLwk+ErEQkZ8APsCOPl9U9ZeKyBvAHwe+DPwE8BtV9d0XafdEHHHI3nQZW0g+dq4NvvpynmA8h0ynNxGV0n7MhQNQmCRyb39OuF44VyMgIQjkzEQmTvNAJOouD95MWu/hfjMgmOck/PeUEsejEakqolQv0Vqv+T4Uj1eP0P49ZfUJXuwOSKRlHK/lQvA5OUYxZ8lpIF7VWhJjJEpRCqpZW2KMzKrcv3MXEbEI10N/rxhNHJmKl1JSuAwzV2HmSiYmTPczq8XIZBzXVKee7flvSt5apr9gIRbRUgjomPOT4JBYevm29opIOyB94VxyXsxk6zc3v+Z8e+654tayNn+gfoSCdau/w0clHC+Ds/hVqvot9/v3Aj+sqt8nIr+3/P49T29C0ZrKPFuItYiJHzn1g23b4Dj23G6tzFJ5Q8/h/AraZFQlgK05q7/o4DlHMIWkJda1MkGdzD/Z5M4oZ8EOyTFpxSwk5MyeiZgDIY+5JXwfpzCXBDNH0MWyQPnFWCwSXdehSMmVQTbv0DoeUwDIdgJ7Sae/HI/ddKqKBB3Oca1IEkJAsxZ2PTQWXtLS/Dyurw6bBKspkYMhWlostkUpO185ekAloCosS2ZZMiF2/4ssVTkpnN+5JKEcliMPHr/PPmT0OnM230VCIImgEgga2cueO0FZcuZBWngkwjtnFv07ISwipIAlRZ4s16iIlMPNjVCRIdW5HzhahRCLzsOvrTX6jOg47PSa0TChKUGoVpRi1wo0a1ObaymLsm4utb2VqAGVo65nBZfydXm9pMQ38PGIIb8B+O7y/Y8CP8IziQWFotYYiZp1Wgd5rkhnSDnd2uo9R48Ktd4SZaTKofW324V9Pc/+1W8iwlRONr+rkc+98Ra/4AtfJrxzACz3plBkR1dn7JrdOR6PaCWM1B3fdAtRQjez5nyib7B2um9EStrCtnuMRhdTqsig2ZlNZcvFW1u7a9fxre+D3wUmpoC6fpVIWTFlqUhsupKqf6lTGkLgbN4hd+8Spsj1cs1yXMhLYqaIJGcTIpMRjnTg/nTGfCm8+8E1cli4zkqaTEc0S+RQ+pYXCHEqHqHKgrHGM4FjoBEtLeNaFkad/GHM/fvChpXKWYwEQdzxkIpzi5eVw5gTo72ouLZwtecNZ6is7r0k+KjEQoE/L8b3/EFV/X7gM6r6tXL/Z4DPbFUUke8FvheA+2f9RmHbS6nRAauGqG+MwYn9GUY2cyU/bjRw0h7QCFWU0NnO1J8X1BDw7nTGp8/vcpakRIUKXtng80FWRqa1r93Vu1zs37OioRAJ7VzAjUrISgAYxZVKeNtYoYQImk3+r7EhzQpVESKZ70c9hdu7cJ+a704zbJUXGq6l5p9hHFHWMYCNQkDqu57t9pzv9jwqBOmYE4GJVM9LQZFoRz7uZMf5fs+TpCxklijkjKXsq4Mfivkx2P5UN6IEEMOgDjSdpTPzOm7WiyledzPCxhjVpaiFQLh6Q+kayzKIzGN7ldvYetzTvFU/DHxUYvErVfWnReTTwF8Qkb/pb6qqimwZrqEQlu8HkM/ffwoWV1ixeBuIfyKOnBbYIBg1LX/arreqU3eIQQTKmftnF3z68j7h6tiJgq6R5jTPQ9dbFNOpdnfp2ZlGqx9Kc5cG2xmDcQW5iC3JKbvqaen1tdZjZu/SdSeNU3D6ht6Brn/wlg4tvhTWXt0xx/eDMeYk5TQgldK5DuhytogQVcglvH0+HDimzJISU67qV62sCKj1/3J3xqPrBfLC49Jm6lK9zVkJEjM/mMrRlv6sloGqdmWodqSWWMVRTMywVm5eRyLu/FlBt7jNtjmGLkJ40WPtfVrqNG55vbydXuOjwkciFqr60+XzGyLyJ4FfBnxdRD6nql8Tkc8B33juBtsi2bo1vnTTPbTN6ylEwt8ryM0KaU1w9Au4P9ePv4gdchNc/RCEX/DZL/L27i7pamk7TqzqEckEMvVw4pwXe14urt0VUWtPHAdQ/2oymxhjT64TnGPUkhpCtuQ0wXKEhGBckaYFYpfXAZby7Ppc083oqCOiEz1TTZQ+RUHVe30a95eSDv0QIGXzz6gOzZ6dVuD6+rr1YS7m3ooMkwRev3uPs8lC3d9/8JDjoeTVKD4ruyhEsRidt+I58wQPPnifx2EpxzEYvmcUSZkwBarlIxJ4QiIqHP0SCoEeKVrGI9cCNbFywW23dsZxc9eKIlzr3LIWY6w3VqE/c9CzbXiBtjYcB1I5y5cJH1r7ISKXInK3fgf+aeBHgT8NfE8p9j3An3qO1p7rmRqknUmBH8BW4OUOjjW50abbeZdsC+bexSV6fTTvwWziUovOVJrXZhcvEuZoVTN59508hLomNnZ4r3txi6lyBr6ODwtvyJtyK1vbqPd9oJi49vv902xa3uKxFkHqOLTAMk3Du/i+ek6kxpGAcRlBZPDwrPqPWs6bh6MEpnIie7WGxBCaeObHrf7ZwUhympc1GIL77Ngq9S+4770sQcbr5U+FId1BIzQx9LqVq1iJJuKIwABS+la5349h/Xv4KJzFZ4A/WV5oAv6Yqv45EflrwJ8Qkd8B/CTwG5/d1LNf1MckSDlfQwvLPRbU8fOmgd58iN5ImSuLFxwRADPl3T2/4DzOxHxEtUSRZlOYTVLcu4vuoWrgFUt6qy7K0zgHIZWgrDmaUjMipo1vxKTvSBVRfLBWLZdSaid/TdM0ZN6u99dighGfYhmBVj4GMR+Lsoda3fFktPrdHxPgCUFO2nQf4Nh9J66JCAsQHXdlfRHOdnt208yT48KDhx/wwaOHxBgtsa+KZQErdXYSee3iDt+ar0iHI0uJOVKwoD6FUPqSxMSUgcUXaSPgd22vC/J6sht3cb/2KlcgVYBSs460MmOyopNV2/RPI8chte2T4qucHh8RPjSxUNUfB/6RjevfBn71h+5RdS5h3PVOhbHhoaf6iKeJJE+BmyZdXPtVsSkSiSivX94lL2a5OKRs6ocERFoS2WpRqCJBzpkoQpIxpqMeHzjsuiLMG3304sR6afnd3e+o/nOrvD/CcCSYva4nAp4Y1MUZY7QT2Qtn8yx2eIvb6ErQ/tyqrzmfdqRpz/HwuLjJLzBFQjBCPGE7P2HijkYOaeEqJeZJijetEa56YNMcIMVAyLCINkdMU+z291qP4/PAqJvhxnVp5UbkrloWcZGwvj2pug1AbzipvROV5+7yjfBKeXDK6p0qFbUf5T9HXZ8PCuv3XAdZMnIXfqKzHaxHWzA2gTsCb5zfgSWRMPNfaLTLCF+Qnqy2TtwamS3gqSvZ/ALd0h0Y4pQ3zJnkuAr/5wlRM3OqniLAQI8z1awp5bINS3f3tr5Iq2T3CnHPy2k/UrHG1Ol076WcEoy1Itjvkrs4cb7b83CxtHxhySb+Yc3XLBE7FS41EjXwQVJL7EsgRGFJNf7GxJBZi6JYc8uz+iwnpmcRwbVFzC7mpyLuqKSv41xSGwztAhqGcfkwfXwReGWIxeiVVqhlOkKLGiwsNCMyD/ETtbGtAZIq6oSxjFN8AgTNjq708iUXbynT9/Evv/Fpvnz5BvMT4bgsyBSZFyVkRWchEJoXYSzblUoP6KocR0pHoLOXOWdC7DvrcEZH0zd0ReL1Yel6AgcxGntzXCx4q2rN2+urK4cPO1dyXox7cu7bfYi9yFKJXm45LTyBCjUZM92dHCCXgQ5RWy4LD4vTRUwtpkQ5u7wgzBMP0rX5p2TlOllf7VyUgERhEuGNaeY47bm6gnfzNYeUyVHRaMFoYPuIRd4YZxLLxiTFNdylTx0Jtz/Lw1tBKjGVsXLLCLoqB6Al1dqgJ/KD4TeR3qCJMpxuKLX91tZL8M16ZYhFBz8zW+zXKbvtyzyVkop8aHZMVnWrKPLWvdc5IzKFQApaTGy5BDBJsxyMfe3cRb1+oh9xn6OS0RDLdBxdb7FWErbvZKqTWyoWl6bcU6hORv5Z2YfNYw5U+HqsuL4VVG5GVVvqiFa/tdsPLqpKzHogtUcY//71c4qRuNtz7+KS6+tr0tWBJ0sy0j4Jc6yhesIum8fteZx5pItxC7lwilLFKiMMUYSlTbNbhzXV3glCrhaT40pvguE0tLXofFJ4Q1z0l9zz6lYpa/xoTa09P18cXjFiMQaJSahZK0fou5q2wfYL6tkEY8VVWKNNxKks943QDvcR3rh3n12K1OMLstqiq7stPm+Cy7SkqcvhNUbC795bXpoNqfVUwebLrpG/OTw5rmWa5oa4nuBUT1Hfnu+TtyKsCXUt084+JVggnRedoIs4K1GotlPbqmMCWG6MUmwvEzkIF/OeWYWjBq6fPEQkEMQUyVEEzbBHmInsJXImkZwyWZSw5CbKZVHm4krfcK0QCCl6ixoucCP49XdzqV5cHcFYQRsPf+tZz65EqPqd1E4UN4GXIYy8IsTCfPztq9DzElbLQx6VberkuiqGVKuIRx7VccCrzqESjPVOoBYvYeZZmp4kZojHzFIGPabMJLDfn/OpNKPHhSUIUZUztZwPOQp7gnkYSlFWL8kRJCWlYzEBOuVUeY+cEzFUgqlkSe008xjLashFPCovWU2VtR2lx1yoarMwhDBZdR9EJrbTHpeDIYX0U8eqtrYTjUiQ2KJS1wg/SSzeknbQsRGsqRAQKRxRPf+kBHo5NrtHZyakKC2bvwNwVEWmwNnZGfOZHVT0wfGKpbi2SxT0CDnAEiCq8JacsZsjH8QD38wHHudkMSEYiy4pWwb1ycTEFGmenELRX5T4HJO81B7g1rCNcxrXYRVVsppOx9iZQnieoZTM4q6dllPNRfqpmppRBKEQv8ptfEI4C6VxEJVKehZLOwF5Jnh5sCqJPIEY7vkudObTkpL0dlRNZ0BNOlPMh/v93uz3WRtr244SKKZSsppjlEhje7csCaraEv00vQV9t64QwphDwe/8A3fFthhT2/bXcq5xmU5n4eqx+r7FuQ2JcAqROqZuWlU1nYJB769Iza1ZTLdl7NBysltd8H4PwDi4aZqIqsiknO/PeHJ9xTElQu6xQ1G1cX1nGgnMXOXEQuKxFHN10ReErORguqDqFu6hcQ2VeG2hX11bm+vrBjFmJWZtwdbcWSXG83nbs2hrXtET8eTDwCtCLDhV+tQBbwuwxGZIPp0IGFmvFVur/cfNMmJrs0+q5XSg7bCLKul4ZJdhN+344mc+R746sAumcY9qO9SkEcVcradgOSAlSyE63Trh+3g8HgfHLTOjdva/InVKiWkqQWMSWlkpHHQu53rYDhfJx6X4aTg2ulhdPMFKuSsTVbWfqYnt/l7BWp/pCcTgs2Hb8UkmLSn6EeOUC9FECcSS2McRU8c+N/0KTm8hwQLlUKYYef3uPfbTzKOrJzy5PiCThbIfJ4oCs5zkngJvyRkSAlfh2riaGmWLNOW1Wmda8pzsWP1x2ax8LTSsMHdjjW2IGSLi/E505AiGMXTXHDEbM6/Xi15htN2lF4FXhlgMMvCaEyhggzlmfXIN4NEdP4FrC8jQ6Opa4RLaLddOzhkJgZAyFxd7Xr9zj/h+QMphQTlZOHST1538XZFNh/Y6wlZlYyUSXi9QDxRe7zxrD8rGZRRRZisbVhsu5MSJy3M9JtIIoiV+IjiOyM2Z//QgIoQo7WyNNRdVIXCqzKz3a3q+GEvSXfesFj6vtIxQ+90OgOsPDizHhTnsiMnGNsVAUshBOU9wj5n3D9dcoxwjEMUONCoHFgUszsZ6EkEzhc6dcgInXERgOPV8C5w4oqojodgYyy0F+FiIjy5nPANeEWIxkFh33RScz1RaQmOdYT2YYZwAx7lIPl28Q5uKY1aMgInCLkReu3OPs3lnCrWUS+xFQoMJu75ejbcYzurI1e1a2zs21nnl8dl1ByPSZ3cokXEV43tERmXkyNGsRCHhpIwnWiF0mbiKOF55OSB8VYTWHdKNdXKiiVeWrglGCKEl8llH2VbntjbKahnTq4I5hADJwtknMYKkIhwlkENAlwN3UuDNPPNeuuZhLvqoIrLUsQjFZ1uDcQySExpv3qIHLnZNMDynvBLpTpSijQPpZXx592NVZ9wMRsnko1OSV4NYyOplpNJ2EKpGfIxU3ILcFKK0iRkITa2f9YRQdHnalJDVsQotYdA5M4UJTZn7+7t8+c3PcL5guxGQF205ISTEzpGUNpIuSOqcgkfUgLGuPQHv6HVZ/RY6Upbo0Dwi/HCKmVSCdCSlI/v9OUvuOTmro1gIwdL4FyQ7Ho/DOFe/iWVZEKkxGruSJ+PY9Q6uj0MiHS3IP4XhJPZaztjp2Divmo27zmcqyWKIPhW/nZw+7eZWfpomSwT85AkXV+fIkydcHY+kKbAjoClyjJDnAEzMKfFm3nOx3/HV/JjHMZkyNFOym4FqaP41qXCozT6nbscvBGDg3KT4oKiC5E4QblrDG+bZtj7rRrUiqINPTa1b1x2KTN2atMlVvyC8GsQCqIl5yw/70FFL7j9vVPj4MtxEUU2CrWXXbLg9tOyMpY1YEAyUN+7c43LeM3sxsRCYmnNiMC8W/UJV3A0u2EpbTHUHVveunoXfIpadS8k9mEt6v6G7SU+uft21U+6ZuFW1m3BTF2Uq1+bzfHpk9/0TkRYwVYlF7UsNrqr1Wv1gGcx9TgtVZd7tCCmZotPlyxRGjqT+7fd7AM4fWabw46OFRYVEIdrZIk4RIz5xEWaFu9Oeby4PrW/R5l0Q7AwRi/I1HYI/Cffpu/XIAdhmcCpqj3XEvKxwgt7AdZzoLJ4G62e9BHh1iAWAxG4iLSxhYbhrgIX9TqM2HVfH9BWuzUrdq/uliMnfOVHt/dW/Q1UNoUo7jQBgO45qYlbhO17/NHeS6S4ELM2dbRsNWeJUF30qRMbpGKqSU3PJhibtWUpHSu+qfSyBZqGGTTOaTHPhDkTEeWkmVK3tlI7EOBcCrCXt/4K6/BRjpisZEDfE/r3vaKM/RyPeZZxblqlauvZv6kTe2p5PRC0YFaS45wSEloauzP/Zzk5mlx289alP8eTJE853ex48fkw6LhyOmQtADpk0Ba6mSJhm9goXYc/19YGHx2vevzBFbAgmZgXJxTpm88R6fdU1Nfzc4GYpu/6GGFHerI/PU7SRw0Hd/rm5j09t1Eyr2pPofER4NYiFwuCPOuz06eR6/9kprafAjeBsPuspVFYLk1nlclZyfDKN+cXOnIEqi2q7UCEcQbo2PGs7wY6UnUIzUcO9K6w5pnqtfd94Z6/YHMPRT1/NHLHEKRQ7IdJSqYobOWdqroXWvxuGbIu76xmvxH8Muoetd/ffT7gotybIapYgqdy3eYCSMkjgfH9GwNL95xh58uQJx8OVIZqqiVTzDHO05DqivLG/JBwz75eDtPtjtTCi3WFvS/ew9S4vBCfExT2fUyXnMB6u3FBfXdlPDLFwMLDnm1DDhZ7dRhUnNqEkIrFJcHWb7Efph32JYrk0YoKLacechJCNSFTX7aymt4hSTH0hm1FQLUJ0fezigDC5sOlO9NkiFtCVhJWzMEIxOvh48aDqHXzb3hsTGEWHfIrMa4JgIsvpsQy9v6HrogonE+MM9NB2ymnhnRMZiWT1+KxK1XbPmQxFLGeFZDuyMITApMocIofDgWsxohGWQ9kLtOQSMW7JXMwzd+MZEg78tC6NqxUt7umV2ImJNB+WrT/ZwPzudmOZbSKCIxI3EqhW98X7ugWvDrF4pnzVEc3rFlrWq9XupH6gpGiKGxtYkMYxAZWzkVoWx1UkJR0VsnC2v0CWjB4yUeamh5CsLfzcmAtthEJzJoo2n43xtU/Z79ptb41Yi1x2SM5KvNHRHdznrzg/Px+OHVhWVonKmVgezm61qc8SdIiclYKEW32rZmOv08h0UScVxexURIkgq9OzKifn5nFy1phQyph3qHFzu7kcM1CItgBv3H+N3ZMrFmZihickFhQ9LlwvC4uCzpH92R59fM1nz9/gx/QRmdzEt7Wlxga0cnDO69jt3qNe4oZ1SSes5n28gdG1fl3n6zYd8ff5LDyXMerhPhq8OsSCLgdvHjepfjAEqmS3IUbWuA0bWO80QRnofhRdK1uiUpeiQ7ADdoOdio4lfw1T4M7ZOXcfmeb80UXm3rWJJmoRWhb7JqGLMNZVjiuz+3ohZmGw0HgT5iBiiSDlNPWaF5Mg6NJ3mHY6VZOB4Xg4EEsG7ZxTCceuPhZmcqwh5rmdUer6SyAXS0FuaQWDuWNXZCn9q8Tbc2minbiE+j6FwEYJTWmZc2Y3zSQXzKYOQQRBqvI1KzHU80P6mNb4lsPhgEyCnE3My8yyKDFn8m7HYj7eiAhLzuTzmWNWzt7JLPvIQbCDloMdsDypnVWyhETN8FtFIK1ryXFAWyJzm8t2r4pl2kTBAbnrn0f6NWxxFdLv2didFvkw8AoRi+eHrsS5wUrwNPHjpBzjxDYxSIYsXCJmy797eadMXCYtC6q2WEJ5pOkG6o7umx2p+zb7froY6rv5vJy1vjoFozddNgQjOp8EA680tbNGap2yaBuBCWRNw7PXfbLPvrgb8XOEcGsn7e8UidE4C28G9Eq+LQRp3FA5jd0fqFTfsYpZ9ayUi/0ZqfqFEMi6tKDmJcC1JnRJzCEaIVn1J2+sqfW680T9pFzlEG7SNbh3cxVH7uEGjsVfGzhq3wfdWl0vBq8QsegRpl48WE+IFtaz3lvvPOXG842MYx9rbUtyUwhFUiCbh/kxce/yjM996tOEB8o8BaasTWFYPSyDY4ta7smKoDoijHfSMr+f9fF2gicWrWwr1y0Y1duxlq1jtSyLhYCH0DgH3xbENqZrwrvWUXRuZ0R6LZyDSBg8LavlxkSG8YyR2o9OBO0zhkiIEdHQfDuqvqUTifIebnyqz0jOuZmF9/s9miMyZabdzP54DsAxZe4GSNgJbkdRLqKgx4WwQCAzReFI9/+Qybif6jkqUo9IWI3bhs5hzW2sx3VdfoB1uzcRjIFzhhZfVW+/BPfOV4hYQM9wmk+DYwboLGpd5MXWWRuyj5bwZgUiPYGM0oiNydvV3yFbhvhgk7sPE/cv73A+71BLUz0kgVXUvDm1y9VVwSfan7NWYFp3ZJzY2jcr2L5Xtrd+Jmhu2CP30D1EPeFoxAs94RaG0c15yF7e+rKCttCzmhv8QIQ65yEiJX/ESKyquIKOjlyeQNR2PEcE2ghgJUq1fK1blbq7IIjYWSOXZ+eoKk+eXCNRmENkFycWlCtdOB4Sl/szjumakOyZQbEoVtWS7Ebw2a78fFLnq+9m/Vr5lC3dwkeBLS7jY4JXi1i0wTUuozrS2C0nv7sF/MwBXyGitW8DHDCdhW875hUVLuXu78/4zk9/nmkphCApu3Lalf0zpVgoRkeR4tTV6Jg0h63kFKhhteiqMOJ38qpMrMjRokzDKP9anaVdC2EiJSWFhBCR8jBfb1BMNr1AQXqhiQM5Ly0/p2r3mgwIIfbMXyEEAjWbdxdzAj0SNoTQXMJrH+p3r5Td7XbU4xJACSVp8bzbmc6ljEOMkf00u4OSQjkXBDQtTFM0ncxUxJ0szBGO+QhauMEM5/vAG/u7PH5waHNL0SVJDHaUR1Ml6PZu7cSwNq9rceKkiivryvvTzPoalpFyO+64FG7Xn8rBfAh4tYhFBSlKSN2O97ciMiiA6uAMFN63t9YR6CsXdmcAACAASURBVClrVhFm6/rZNHOxP0MfXqMaGhEBDAkL9+C5iapTEOe5CZhPhpSDhAd/ftqkDwpKL26NHRvGo/W/XK+7a0vq2hYsNCuu6EnbW7J4/xx3xhYHUstkbTtxNQd3Xca6+6dJfDwXMccJDdY3IyKF+8gg0onbJN1NvBJZ7yU6xwmNgUNZT/v9nuvrx0zBIlcVI+BBhfN5T0wKSyJny8pV3fhtvC2gzNbdhu5hSxfh7z0PJ7BFWIpo3cZKGNoZFKMfE6fxEjLzfYywoa/QPFoJ/GJdD65fxM8CX7clbHF9eP3yLnJY2ElncZdlIevCcjj23Rx61Kd2UeVpbH99l3U/PAFYs/hr6AFa9b3DiY6gcy/9cObOC43lGgcwEJ9VcJnTXYQQmKRzPi2Wxb1/jQ3pGbM6N1G5kn4kQuyHFEknFH3CAkGmQWypn14ZLFNk0f7cNWGMhdDU5+9UmMpfHYtxHdVUCUVhmfMpYfCim9+8bkDkm6wcw9rdIkLuXhNdm0PcKlDwE8NZOCo5iBb5FIn8ocYMc7QaEO3mqCYb198pDwfKiHCCiCklS4qSlJDh533mbeZFiMdEmCaur6+Z9pMt4SB2VqkoOQRAkJxaQlgBluOxiSN1cdYFvixLW3RDiPlsLPna87EiY2b0c+iyPq1ejJYroo+PpfKvoo1XHKLSHLIyStTKSYz6hoaYwawtMUYka8vdWZVNDWnVfEzqe5v8T1MKq6qZTysRqoTDHYA9IHjuSByLOAQwz3MjOI0bK6bVlDM1AO/q8TVnuz2Hw5VFexRCh2Tu7c6Jh8R8PvfxPi7k4jE6CyiBRLKAQU4R+ESsqItsCyoXsXWr+hBBt8yV9SzG1pxwNPX5L0UfsoJXg1jo5lf7vcqS5VnttfZ+mBBxhMfLgTo+Y639ry7BFbFQW2zncYarY1FWast3sKX6C9Vr3O06FUkqN1QRQqCdVVotJq1/eaUEXX+q5xbG96jPNB1CsUzIaH4dcmAUiDE20cGDf+6J96fqxigAktFcFLBFRVsJppEhW/SNrS7QOZt40uQaCYZI1azkJUFW4hSRICSxvN2p7ryOW2u/6/uJsJ9mohbv2Fhd+ntAoKXWA8/prMfquRH1pnJlOE5OSLOHPfM5Wsu9ZJHk1SAWsP1iIlSTRE17b0U7xd6irKdtrELSVwPemY6xrQnhkDNRItOiJZ6ts8q5IKtlxArNg9M/y8viXnNfEeaE4G29QuuWjIv9hrXmd9cYY9u1G0kQQSSWWJVleHfTQzhCsDG2TeyIUylnCkgRhvc1QtR1Dl50yW7MLYHNOAdN3+Leu70fYxLhGCPTFDhcXQ8RsSEEZoSosJTdOLrgthBCS54TimPYformLVq8KrXNUWjDHdROMVsribcn71RfdnKvEq61tOA2uTWs1/GN8BI5jFeHWFRYixOV5ULKuKW2CzSzJ3SRw7XTJrdYPZ6mxGsTr7ZEQrJw5ijK/csLpkOCRdnPEzX/SS6cioQeodkUYHRuQtUiTeM8tcU9PLPK7hKaQq/Wr1CR33t2DtyK1qN1ul8H1PIwzztCIQIZ2uHFqmNiHOMSqjfiKIJUaG7j0glNtQBZopnOtQAtlsPrUGoKOyTTk/lWrkNMKZsyGrp4VSFKJ8SxOGad7fZ88OD9ISNZrtG/xxLmnin+M+VQ41RSDidjQ4PC+X7Pa3cueVcfN2JnHr+2/oJWW48+FVcHhWNZV5vnXA2Eoq/Pxm2V2KPNh9UcGK4dbT9rbpXT+J0PC68OsfCD4alm2a3XO9amFnolhjwPVbX6blLLYu27Z+Bst+/nlSbjKpZlKSbDTiiGA3ucmGR9fs6Ew+4dhz5u7e7iQshDaJYVrdGzpS9VDJGSQGZ9/GBr7yRGxTsbmUxf6+SckakjcNWDAMjKR2JL7wBgUoa5e/vjCq18UUiGbeVuCN3Lso6zP9jIjxNlrALa/D0qQWzLRLq4NM8zHDKEiDh8VCd2Wj/99efkNG6AE4Suc74SP58H8f3zh5iSjwjPtIaIyB8SkW+IyI+6a2+IyF8Qkb9dPl8v10VE/oCIfEVE/rqI/JLn64aCJvsjW6+aJ1Mik1DJ9ud8FPop1bnVF8p3ydS4kOxpSNMTeS4k0WLSF0vNfz3BVUzsCHz+3hvkqwOqStJMOi6WnFeVgHEfUSBGYZLx5PIKlauYpqlPvPsTEYtAC4ql2XPE0S1KX0ez7cBCZMnHrn/Ilma/pu2LRVyoz51jZLebmKZQ/qYhQ1U9XkBKRjHJZlysEGRiCjNaFKB2EpgA5rhU0wxWIm/Zw5O1IdlMzdLFCMjMU2h/MQRisPOaZuxUdFly+4uTsJ9nogT2cea1O/f4+td+huPx2ByyTEZMcDyWdA7FzyUIYZ6Y40TOcJ0yyyGhOXPIiXtnF3zq7B67a0EXQTWwAARTbuaQOUx54BJGIufmvX0y/l5/t0Gled5Zo+4PzF8lOTyp7dQ/wx2h69ykijAvSXfxPKbTPwL8mtW13wv8sKp+F/DD5TfArwW+q/x9L/DfPV83bniR1YAald9Q8Eg8kWvX7WxR5E0LShUBCicRES72Z80E6HfjLgJYdUvFJ4OWH5xVYqUY3OqPbvTVX18/u43NDQs25zwSG1d/q0/e8rIlqvmdu1llnIJU9XR+ttrz/fVK0xpctX62d/pajp0YV2vMu+++y7IsLR+HN99Wl/Pa7jTPQ9vHtNiJ9kV/cnl2ThRz+xYtp7jV4wu0bjg3pxu48feaiGzB1q2qn9ogNuux2uRIXwKhgOcgFqr6F4F3Vpd/A/BHy/c/Cvyz7vp/rwZ/BXhNRD73Mrpys+t37Wjg5EDHlXLoZkQNYx26KDDHif1sHoMtka7a4TlezIBtNnRAxKcQxYbIWU/66d99q/9VDNm6dxpcNuoTbEEpIcpwtmpVXFbnJ4/Qni23XXzpbeYupvjnrglLbS+EYKZnoQZzIiFYhGyYCrclhCkadzYZ53BdcoWGKXJ1dcX19TVkZTkcu2t9lpZwqOpZRKTnIW2xJNeFC8toXjjbz4SiGBXNLaeJEYj69/zQ5uVFkTarI07bcNMm+LL0FB4+rM7iM6r6tfL9Z4DPlO9vA3/Plfupcu1rrEBEvhfjPuD+ebnqkd1F/a3e+4SSFlnSvtcVJ72yPsXiIPXAZcZAr6zMEvj0G29yHmd2JYPSUpqOMTbTmgRpXAUwLMxmIi27gxal3TqNnUc041a6VUJEWg7KqrQCBsTz4zE6CqllHRchEgtL218/ThZg1jJuRQtNry7fYEMa8F6SCVgTXqkcc3t/AE069L8qKz2nAMVkm2UgYjF2kc1zM5eXlxwOB157/TXu373HV77yFS4uLpoIMs9zIxBBYjtOETUXbYsMroFnSwm2g1QsMGcxso92Dkwu2XklZ/rxkyYq1vW61lmsoR02v16DjTOu4sJ4e0tP0RWWpVEpXsC17VCo7voZLwE+soJTVVXkmfv+Vr3vB74fQD7/2kb9AKQbqGpX4HnwuoDykKaYw0/oBt2o+S9VlXp4iyjcPb9gaj6OYhud15toZc+LZQLHriNtZ6hJX2sHTxDcTaonmVvcUF1762vrcfDcQHtGMoQxvUQ/QM9nD2/PdO/klb43cTc1Z+oW8kw1ZwW9L/VvHX6//u45NiNY9qciHNPC+++/3zw+RY04Us3H2i1G5sjm3MSnyZSZV08sM3ghIALMcSKKjU4UI6BZvD/JaX7Rp4LQQwzWOoQVobhJrBh+S/vvZw0+rLv316t4UT6/Ua7/NPBFV+4L5doz4SbxoKboP52MnriWlfwr64nwGs6wgXyu6VxNVUV+3c87glqmptn5LLSFrv18jhZDsGqzd2UUN1ZvOnAi1X16Km7b3sOxcVPlTxgJBNgCF7ET0eq1Fonql7xyo0v6ljJ4TTBGMYxBV1CfN+o0Ogc1jE123E19/6J49XoVkThwZI8fP+aDRw+BjJZAsmVZminXE6T6zClG9vs9IRjB2O12Y181MDdfizJvwfQpqsoNLmjbsCGm+ntPg0FRutY9iDn3PVMnUe7fyFm/AHxYYvGnge8p378H+FPu+m8Vg18BPHDiyjPBLzwZBqESjU4g+m5s104UO8PEjKbCTV1D+QshNEIhwBwieUl2DuZitnlDXhnk3+gRbeWIpKocj0fS4Ug+Li06ssqknnAEOOlfdfJaWxgGub/V7zoCCgJUR6Vap7LlhthWfnJE0JtQw8qRaT2O0aW4r+xxebQdwZKFoIF0SKRDQhfz7SCL7fpZyOlU+ZpSaoRimiamaQfYu6rAkhOH5chf/9Ef5eHDh81Rbj/v0JS5urqyfqUeozJPE/tp38SVqoeai7izHI8cro6QMxdnl+iSmYkEuj4sS/0eNrmxhpQO0Vvcxlp34cUQhwObOoeTNb3NzXhTr683ZAX/kPBMMURE/gfgu4FPichPAb8P+D7gT4jI7wB+EviNpfifBX4d8BXgMfCvPndPpHs0Nmiy2ciKDmWKIo513QaryNVCpTvV9rf65JkYWSIfl94PDxWF6h3TNVj9LeerhmSrthriu8f4IC0webq1Q98tvQMSJSlwKK/VdvmUTneX7N28i75hW0Kz/qysJtLGXQakrARDVckuz+egn0i5JfqR0s/o7gtmnrUs3gV5ijk2IByTcnV1xbIcePj4ETJJC+DTnOzYhClC0x25xL+aWYw6ARZPMi39YKXqzGbiiWVnr165KmbpSpWh1SoSPocUXtfcmutt9xksfTe10WB9KNGK8Ay49CwR6TnhmcRCVX/zDbd+9UZZBX7Xh+mIZsztloCytIHVPBIJzdnF+SfbwkROx8NT8ZsGSwAswSxlF6VldDJEC1OEpTZlS8N749VJMaT0x/2dJjlpiKaQGC0Gk4/byEomMwUX5akW3AUlAKtmuC7h73OcyLKgGq1UVlOSqnOiEkOqGAKHpZOsqqcxhDa9Qg5SztU9NcnW8akBavVaIz5JhzHJjnjWMZqANHWP1i3HK2/u9Gvg+vqaZVn4m3/7K7z//vt87q1P8d577xAQdtPE2dkZLBO7ixLSDsg8EeT/5+5dfm1bsjOv34iIOdfa+7zuI51pO512mcIgTMeFDKJRDfcQdAp6xT9QNKokGiABLehUD4SEkEoC8aoOD4kOHRpQKgQII5ctGVfZhbETl9O+dt57z32dvfd6zBkRg8aIiBlzrrXPPXnzGB05jvbZe801nzEjRozxjTG+4ZsZei6a3zTPDM5zM+44O8fp4QAucDsODGcpWl2p6C4gZLyCil8EQDce2n1aZ10fd8uOK7PShM4jIOjFtoJztJWqe5ctoe/tAp3vRgTnFkPYag8Xu9eVen2MbXvELlxpDUugk2QtZC3re6jqZV0ptza6WbDd9XpTwq3ZrVc4Q72ta6rmpunmnp2s1d21CzS189Zx1LdeM8gF2bcw5srKtUROalnBdRWmvdUoLrEM+86Efe2TXuupDN2NipCFv9TY0aVcp+bZaNEy+kXDKAT9MPAnf/IneC988YXn/f2NeS98sCxUBzLPDN638HbxWrDugl2EwDhYCcQwnZjzXMDrzBACgzhLFix8rOJq4gGI5kZitPTLpm3MC66M0TY+mkbQgZvVy9E07E57eGxx7LTx3hR6E+Xn69q7ISy6ZvVKHSL5ws6qEntlklx5SdcEx4UA2kxUUVph4Z6Bqamn7bBFeDgn5LJS9sBn3a9iA80G35gWq9yOzlVyYQOX83qkpLT299L93QGLpi2UcOysTW21Am2GyeScW/HlxYS67EcpmttyT4tJ1fd1zTPZ9nmOCZz1h1ePjh12VDyQawzGr5LhUId2bN+73Y7TfObLr77iZj8a7hATg1ho/hN9ii/P6b2HAhBb75YumWML3TfL0XCNUDCR0TtESzSs801Lc2LJZxl3ta96vOGxBezq9l4T3oDfiyC4sq1+LFjWas3bmj0/Zns3hIVq0Raq5LRfrgJRGw2iJZNBmzwXINLFJTaxFpv3oQLiHXm2GAKvsBPPDtgDQ4bkYK6DwcnKDVerYPpgHhNV40Goj+NZwEPnHAnFd3ZyTKnFapgWsKjnuTMjKn2+CYVuYparKFaEGWwAebVn0SkaUVAIOOdJWMlDL96eOy+FiBq7lRZtRIVcA7YULC5D0WiFhlwRNILV7PAYxjKnGSEjQYlpKtqUZ84OyTDPZ0IIxOgIYRFGRuFnXoqULEzcebHw9vKO/95v/AZDCMw58fnxnhvn+ODmKWMYuJHAqAOnrKgmUorgFKehgaPqFCeekAViZO9G9k9GPo2ZeJ4Y5szzWXhQxxw8QxZCFo6jMnlAcqsyUcdkG0eummdtiSlrk18whW4s9kNRSQsYlq6YJFttpT/HxdgvGsjbgSzeEWHBFbv+EVOkX7m/rm3Psb3GY8eszAVZeCRzVrSmynfq3squVhMYFma9XkGu2d8rNL2/Bzrmqs32a2j5tX6pGg10yW55IePt+6HHDZoQ3KRJ1qC1ev3qUVDnFi2wRKD2KeiU/aumNcSEukyWRMxKGB0wrvq8XqfeYwiBFO2aL199yUeffkwSAyBVlKiZicwoyixK1pkpe2SmVRNvOFjROFJcV6uvVH7JRVQTYxg4zlNx7fTcGvVddX3evX/tzYXVS7p8X+UDb9RepyVsze3td39uMIuubVVbwNKUu9yMvtdfN/G/5kJdB1f/wVpYNA9FuZf2r00AyHmN9G9NiB7E7OMwevt1sd/16gS2YKI1jtLjFbDWPnszxyb0Jn6iCKY2IVnnhLT9moliV9gKq4ZH1P4s30X6UG/Tuvr9U8xEt1Rvx/kWddkLvfq3uUutuFEW+OzhFS9ffcn7z5/iBtOyUklVm5uwSKQEMxkvIM6RFHxh35pjbDEoIlaBPcbIMAxWnEiEJ7s9h2kmFpYslIVbREHX4XPL4pQ3wkIfER7X2kaLXn3VCevaR9f2+7Nq74ywcGVQQZ2YNImodIucCGRD49vgzR39WDe5L8JIegHxSAdL3a+fHNlcg7lU1pZcRo2shUNLBS+TP1dNogiKRs3vHE6leQmqIGn7FtOzDz6q8NZlktQCfdX7GIahsI4vqnAzb0ocSRiWOhteFrZtI4RZTJHaqomSkhXj0WyYUi7vrqcRqPddBUDOJhwsOtKtsJwQAnmaGMfxigcpMwy7xokaggM38Pvf/z5f3H/Fze3ITgJDCDBFkpuYh4E0R5wakc3kMnmeQQTxBa8ZB0J2TDEyJ4t7GUpyWTUFNWWeu5FDhvmciHtbULx6RvNEM0k3YTvPUGs95lYXoZUpfGW17zXDK0PUQM8r27gyrt+CNtG3d0RY6DIhbMnbfF06uanPoEUbeNwcWSeHSQcgf60JU21wLQFEHRdFDwYmltXfy7Iym+Bbq+ArotuqWouBZG2QaVNgL0yWuqKkjTZRVeBes/G+MEDlwtHZja4KklYtpWpFXiwnfAFcFwHUNCMFEUeUTlBlRUusfF1Vtyuecw6XneWJuKKJ1cmjCwAcQiisBGbC5Zxbwh7OOCm8H/jycM/ZKQ8aUXEoVhckFnPklGYUR3A7+kJMwBJ3wgKAVkE4z/MSAp6O7PE8G/fczfdlAavJZVxEW7cxVcfqRptoGsHXCIj+mKutnfex7Z02ox35zVvQPt4RYbEJkrJY3fbAjz2o1povbh3BaRGfZZ8ral0fq1FjLGxfJScKqYuWSMTcJo0D1Dm8upJc1YVob8Kxq4DINZtRizdDcwvuqUFbzjmL8Czp1b4WDC6DUp1FitZIzGSplO0azjl2u10XLm1CIZfV3ybaggHc3N62Z6pxBxV8jTG2RDdVRbOWws792ypjtVZ+T6U/a9i4BDMKOpOyahm1v2oot3cDUa2K2BgGCIVJKztijvjB4ktC8CAevOcHn33MYRQ+mu741pMP2OWJJylwW9yZd4cHJvEQPHjHMAz2XClD8XKYUDVsqUZ7OsQqlKVouSEPkffcjjs3EUpwn1BwqY5FbG16sJq4LQO2agSPLYRvogVcmwfbgd6fp68r8xa0jG8a7v1n1qqr8OvsPBFZVcG61kySy+pzNVlW9n5ni/fZjWm7j1vO1eMUve3uLgQXq3224GX9znAD+6lgYKWMa6ZKH/i0ufc8RyOqqfiHLuDpFguomZnDMBCcMWlVDamq471m0z9nLwj758xxLRy2dAFBHKMPDM6biZSV4MdVMFbMtUiQhaSP47hocs48GUkzh8ODCQBR7o+H9l7SNOOTeaF0jsznqXma+ndXW80jqc9bzcT9fs9+v+fJfsfoPDuc0SzqYgbGGJsrfPU+6DTgNoRfb/petLpI9uOoXzj7n3ZheUSY8NbMkXdEs7jS3lTavtF5hIuw7671avMKgFSaGqzd5FmOWcBG1yVs9eeTzXE5Z4JzTerX7yymYzHH+nP54Jtm0QOc68csgU7icG4tjGreiiur6W63axhFMzv8Ejbu4tJbVVCkkmeCrrkh6PbbCkLBW8Bb9yx1Ra+8mwB+HFp4+8o7gSMl03R8MQW/urvjnCPReaKDI4mHPKNuJKbEeZqModsvQGaf1p1zRjDvSqUCrBrVko8SuMWhUyTPEzd+wOsEKsw5Q5fzU/XTvsrcErr1Ne3a+G4aNVcF3Bufp2+us8F/jPbOCAvnrODs6pleo1VAXekWH3c502q/ZfUrCdmd2qbFBOknpx2uTZPIaLPlVStxmZZJ6Rr+UF+saslQTGu3p6oSyupYQc46cQA0x3YP+/2+aBT2emokZd1/mqYLEpn622xw239KCdXU7PKaYWk5KEu9jRb7kRJREt4PuCIU6iRwoi3tuzF1O5r67auwzLntF5zD+7Gl9DfMJlh9D1coBqMFZizYSPc8YRzIzhvI6T2/8Vv/F8fnI9POMZM56MQpKu8NI7NzzEx4dsQ5k/cDe7fDOWcp6H4goYRhMNOjuLgrXjIMA6/u77i/vyeOnu9+5yd5eHhgON3x7HTP7BLzzlmtWBbsqX/RiUWjk42QbGP7MSFhA6FoCcsYrud4tL1uYW1azeOHv2l7Z4RFvvIw/WSXPqrtYoV//Lg3bb2KrcUNtxDUmCBroCVrjWGb19DA2vq5v6dqFuRs1cLLeWK53T5XIoRgArTTQHohsdUuludY8BS6e7TszcIS5f3K5LBjpVToWp49lFEWWQDapa9ogrb1hwhBjITOOWdmjltKOYhIe+4lBF1Rv7BmmaAuwscFck7IMHJ/PPCDP/oj2A3glKAGECfNRAdnLNZiGAb26vGpczWroi43Ew9oeFIV2OfzmRwTL26fsv/wBR+9+px8nhlU2Ksjxwi7mqa+GXNcLt61wvpFVOXjg3A5n1zJd+r3kc3nR9m73oKUKO2dwyxa29prq6/WiHvlvNhqFZet95Bc4e1U153LmgVXXT/f1qNQB3rOmajrgB8DVK+n0/f4wio9vJoPNViq2Mp0E6092SP4je8mZgiBMRhWUb0yxhdq6f9br07TBMqxfX5JX+W8b70AXVcZC3g/EMJoXo8yQTN65XlcwzDmeWaKMxI8n758ycuXL7FCcabd1TDuY45EB9kveIHXNZ5UW+WtqBjFMAw8vLrjs08+5Xa354P330cUjvcPOBF2LvBUBsYsK54Sp0teS+vvzpx6owVrOwavtK/VKl5/8J9DzKJHkIvK1kK4r3g07JDXv5CV6nflu3oOAxedBWynjLiAc4mclClFMoGYK27RFQnaaBfVU9HzHNRrNXCzTp6iPfR1LpzYpNzv94bUi8Nlm/D39/fM82z8lOV8rtr8tYRguxfTEEIINqDFgo5CMJwgpUQY96QSri1+wU2Ox2MTECuQlDXAWoVZxRpq7ITvhEkVSiLC4HyrAdKYyCpVoDNCGt8BrSagfbv+8XjkV3/1V3n5+We8+tnBODljicxxjs/yCRFPUse30sTo9uyD9e+cLNgqFwB4KGQ3rgi8L15+xv39Pd/+9rcJ3jMfTsx54vmzZ5weDhwfJl6MN2QvfCGRuZSrzGlexpIYUpH7EZc7QNLezKNjtZyIyrjWY2L1u6uDuf/+2va32N4dYVGaIfqUoKS+7sYSRuyu9kMZeMISbquKlHyJhcp+A+yJZaBGFVSUfRaiA8WRRDnHmTzeGLVaA/pNPbbBvzGX8KgaBX/NStXC1xBzQhCcW9il2wRUR8zKOAQSwjCMUNyiFXiLKaFFuAiQqjpdBNQSsm3bRh8WPs6UEa9klxj92Fyu5i5OuOBbfgy50sm5FrEqzpL2nPeWcKeWJaqqlnAlmSDWH148mktKfRGC2QvjOBZ3a7b8kbIK5HLeOkkmLOp0UCAbQPsnH3/MH372KfdPRvbq4JzBw04V5swhKK985qk4bk7gfSTLzHlvsRTnODcXcyzxFHmOvLq74+HhgZ/9uZ8hhMDpOPHVdOSz0z0nZqbjkXk+c4sScuBTiRyD3WPIftEaRazoVHm2MvxsASpgpWreuFYLXiVW0c3+rsIlI5vEPDZa70ogmN3XCQ3FSmKIzY23IDzeHWFRV3q5dDnVVbna/OvDLgGgtm2zvbatGWC/63/rFzTPM4zVlJC2Oqqz2IKctN1jPV8NRJRczBJsYtf7iimRcmaA1Wpdz1NjEvpAq7baFJu7TnZgiVloGoaiVlbYIk4d7EZjiKr3XzNsq9ZQcxouTLPWn8t3vgirGoIv6kh5WrSQmJCw5Jo0E0kxz4SYAJRi3uwK0Fi1rVgXhpzxwRNR/u6v/R989Opz5Ome45gRMU/LpIpLNuFEJ25l5ot8xgVBg7YArN1u1/pxGEbO5zP39/fs93s+/N738CWZ7pOPP+Z0OuF3gZtx5OZpIOxG0nwiORjPEz7Gkj9DWdV0wVrw1GJMvWa7nvSy/l1MsQtzu+7Tv6t+nPdueneptbSK6jyuYf8o7d0RFpv2qHmh2vq0TqgfFczcTgabhP25l5fTuwlreHaNVqysSn08AyyqaGKxFxXaBK/U8lEtozZrozQqmQAAIABJREFUvqC164Vgz03pvW8A3fb7xezxVgNZpNHieR+Wmp6yBJHZDStRFzr/ykiVu34o5dVXA1c68NXCxAtWgVs8ImEpcNRMl1IZLdbzV0DWbIoyhyxQze924OEPP/0h6dYTfZlYzgBZVZrGN88zc4jkUggqQ/NY1JDuEALn85l5nnny5AnPnz83QZyU8/mEZOV2tyftAjEoSWecwvPbJ2SBcH5JKKt4LONmBW5DCRasmiYLjcJqsesmdyWVXo3LdQRyPfc1QV4XEfrrUIR/MY7eBtD5zgoLWAZi30FbEtkLQXEFqb52Xth0fP930TAgt4mpmlCErGvwLxfqN6BF6JvmAXRMUz2QmYumKdp5XPyCsvdaQ39cvW7P9dB/t/XGSBEOfZnDej3pMA5gxdX5utY/TyW1sfR8j8jCSTGEwSJhy4obnC/XhiEEq0rmyj0Wd2vSbGUM/fJ8OWfuzkc+Pd0x7z1HZrIMhGLD91pPjJnTFMmDtmfZapEpmWYmIjx58qQ9u3OOu7sHBjEX8+TEtJoMT/ZP8KNFdw5ZrGqaePMQtfdDGwNVgqms3eebjnxtP7/mBaw9bdcwiytaytto76SwyMKKsm0Lam4nIKxdk1dbXXW78yxfFX2ymBd2D/b7dDqgJAsiMt0b1LcB0rsy63UaeW9Nacf4HJJa7Y5RAkgxuQqGUgewL1mPuzBY/b7u3PWea5hyH/NgHBOQiAtFn0J22bAFEZxU08CvjhWRVXRi70Gp/T127yNqtHgCccTCneGdJzhzzQYt+IQ3jAAn5KgMQ6W580bCU/CeICYkUyk2FOcEJAYfOJyO/Obv/gPu9sJpB+oHkhfUFyJglWbiJBKf5ROvdGYvgiPyNNpb3+0Hcob5dObm5oZhGHjy5EkLdHt4eCDGyHc+/BARMc6K3YAA5xT5wWcf4yL83Iff4fOXP2CWjL8NZIzvw5VFwEmv8heTpI6/Xg3aipA3lB1bt21vcpcdipZRtN46LN+CKfJOCgsRWfXdVmD0+20/v858qVpH78moXb86brMa1RWuBlJZHRdpWgKyZjvI2iXFlVPFDJlsK04VeIUMxWjjbND2EzXTke4+8lw9rqNiA1d9Hxk64J1fAryKRuA6V2g9jytJZn3Ie9PsSo7M1YHaCfCqzQzDsCpqPO6X1O/qZq4v2aFMOZV4d9AiOM/nM/5mz/3pyBzEhIRmvIR1+QYBV8LNJ01MOZIlMKdIVteZqpaRO3qrul69Q/M889577zHPMzc3N+zHHecAZ03kmHg4n9j7gfB0z02c2fnQNMAaMKd5WWh6k8Si8K7gFW+pXYyKJijW11oHLn6z9m4Ji96+LxOnr2719YebQGjAT8UiunP3+1Z1HZaB76yKEBIzOael0K4LTY2m5G+IW+z27JYVrgkXjJUpVxVflmtVSjvnjDimgoB9mnafb1Inbu2T+rvhJSLmdSnI/+A8TiCWido0Bl9cqmF59T0QKZuJ32szVdCEUl18TnObiMMwNOYvXzw4LaaiaG673Y4Ue8zD9p/zbOHmUoKnOgGtcSbnzByEWY1sx5V34F2htitFgytwO00TsdD7a16CsKpw2O/3OGe5Kk4hhgFS5r333uP50xfG5ZkmPvr4Y0uS2w08f/aMNM08cXtEnblNR9eoFUSWcojtnVXNN2dDmVk0xctJLsskXw/UOmiWz5t3s8It6r7dPqJXhMo3aO+OsNhgBqpa3NTLyrc1HXpgsWkf/X5SVq8rgqKeg02Hm2BYVtWaEanqcd5IWGziO/BLHMCi0nvje9ANTlGyP+d55nw+r7JInXOos0rkrgQrTekSxKz5Gcoi7Or3p9OpBRilPKOS8MPOVs4Uud3f2GQRGLoM1VbjU7VFMvaFmCpxTc2e7OM5Wq5KKvVAxULIb3Y3hcLPF0GyW/rJiVHx0wkgV8oNamomqHOO3c2eV4cHHs5HJlH8fsSfIsdBcMFc0VbIXQh5YPZw1MjdnDjkGdWZZ9kzhgHHkijnvTczqdzDixcviDHy3n7PJ3/yCefjA8OTG777Mz/DbhwheF5+/hnn+xPu4Usk5qYVmZAVMsmyCbrSlCplv0o+VMZbG41bLSNvBEY3jrfjvy2M5ZyLEpsXPLMN/7eDX7w7wgIuOk81I19jzG078Jq5QsUkLnCK9Xm2ndpW2w1mYtjIpfotJSq0hjOnutrV11giMGOMBD+uJntZv1cu2P66eSMctLuf3hSJMeL88rkKvV5jsWhNVsWHeq9GHZwWJ7EcA0ouRZC3/bTtM+d9AVHN1Ko1RCjlBNVVc67ELml9rq5EgQhJM8fpRPaGCwzieAgO9dLK2hbrieQgiSAEJPsCJHdRqM43dqzz+cyLFy9IKfHVV1+RUuJwOKAC3/rWt9g/f8qDWnLZeTpTSYIallY7RtaehkfHoHZG3AZbWwmC7bzu8Spdjt9qJ6vv+21vsb0bwkIuQbzWoZ3343UP3zSM3NcVuY5D9N/1r7Ui683WQFvB3ari2m2VmhFS6elqtbTl3CJWaStFZTpHpmlqK/mBA+OwX4VMV9MpxsjtzU1RocuE0GL+UJPPjP08OnNnikLKxSQAchaSKje7PS6Y+fTw8MC433Fzc1M0o2wuRs0roaKqBD+04K7aYsxGx+88SSeSXuIoqsbkHVNicI5xsCQuKWaJZXwmnDOeCXXSEtGMbcsYtpmT0QPsBw6nEy9ffYneOOZp4gUjAchJcWqanqTMbVYrQuACL/yO99NAnCI4iy6tWkXLVwkDp8ORr+5ecXd3x0//9E+jqnz4/lNj3SqTLxZhMYaB8XbH7liKHxXS3lzq3UiJYSFb5fkyoNYZGzWis2aTvmZ8tm292XFl/La/VVmCsDbneEvt3RAWXVtJWVn0imsD8+qx243XXsgVyX/dzMmEMF6snG1REfMwrEDCEofR2+S2cpcIPiDNZk5UVVi6Y51bmJvUSQt57+3hBV8p2wQcaw3ogl+ju5/+HNs+7UFN5xcBmFKXaNeZWLVfew3L+wFXMB5EIC1s7FkVV/n/y/21qN0qMMp+qHJ/eOBwPJJvrX4rWmgDCqhccadWBlIwYZ8yPtNC2XuTsJp+h8OBMQx8+9vf5qYI6EECDAKScCV4DGgBaF4CoSTGtXHSzr+Mm21fKyCbcgR9v79Jex2AbzwaFnG7wjfeYnvnhAU8Mum5PsBhrfpd7dArKlo73xXTg2Z+LBmJBmi+Pra/CYhuYtYJaJOwAze7CVb368+xVWe323v1GgzsawBkWPg1DSdwjIXjwTQHbzb2lX6t57R6G+XenMAU23P03pk6WevnlBLTNAEQdmMLV69kPu1YTaRCde+8w6e84i01vMhYr06nE1rKEhgXatFKkraIXy0mTTPPWLg4ap9XjKXGlNR4luB8EyA5V/yFFik7DANpmlsZBu+9hazpIlgXrXO94AgUYNu8RFULuMavuX0X+jUm+KXwLx2gPHr+H6e9I8JCDDCsLS/gXhZA09qVWovnFBV8K0T6wC0pRq3i7TxVEBW7X4oqLDmVEoHGWRljZPCCCwWHKC8bHxiy5TqY5rMuTpxzNrs7Wb5EnM+cTwdIEZkdXxzvOTJxc3PDi90N6h3ZebIKESEEywHxmjl3qr7FNVg+RNZMllzctnXFtpXLeU/KsgpB9qNnHG21xwUUhyM3lqxYbHMXCu/FaOp6zItAHcfRqP/STM6x9a8Nfpbq7M48BfOUGW9GckwttT/GCKolv0SQwbwzMSvJKxJGAgn13sycqLz84WdMUYjicTvPsXAp+gyBRFQli3DvlanEaeRTAhVyiFbUWgTIpDQb10dxZQ47y5EZdmPRlsQKH3vF+YGnXphz4uF0JM+Js0IOlunqJyU/8aiYVtTePwXw1DVAjyrJJUztLO+t52Vwi/BtEq8TPFr/6/COfp1r5o4s70x4XBP5Ju1rU9RF5D8XkU9E5B902/49EflIRH6z/PxL3Xf/joj8voj8roj8C298J+WFXuTlv+5heyncqZj1M2wkrFuCVKq2cc3Domp8maIQnF8HL7k19d76VtdBYjktqxOABsc5zXx1d8fd4YGYjWg2b/So3kvR4wnr5i6ed/mxc+acySw0decy2ZXFLGjncIum4r1Hi3mVse8qeU4Nw57nufE11GvFnBo1Xi3k3Js2quYa7UW/qppADiY4xJf4EpS744GXd18SRUuyXy7fFa9UhqRFkLIkBR5d5uTF4JBSqV3UiIpc0U4cnsEHxrAjuAGnHqe+uXp7ur2huFwlW1W5nQ+4pGzH6jVcrTepr7aiiRbiko2N4tc/FRuralShPFgNcjXcro3t/58xi/8S+I+Bv73Z/h+q6r/fbxCRXwT+KvBPAz8N/M8i8k/om0SE1F0qCNQe8jFSjzdv1pdrhm7dmBRNve/61mcYXS1NuAyGLFYrVARwNpDrhG/lAIp/P9YiwUWtjSinOPHqeGeZntjkBr+e+H08R2di5TYJt0+ZUS3eN10EQT02qhEBLyugGriJduxdpkkZUMdSK0PMK4GzSXqOM6pqaedqrFGNUWvb71pzaDpWsfoOaj+LIzjTjKYs5DwbXnE88HA8Wo5UNnZxL/ZeWnXzctUAaHagmSkrMxCishsGgnOlIPTAEHaMBYfaDXsAhrBbsJqCfaSUOJ3P7d2lZNwa5MxNGBHNqC5mTo1laDVnHQjXMQobTI4LK6PmCLyuPSZ0Ovyo/e0clxf55u1Nqqj/ryLyF97wfH8F+G9U9Qz8gYj8PvDPAb/6NVdZ2JHtoqtvZdvbXyet6bCLorFcqGRW7BMjvHEYkJ0Z1AKTvMKtCzz1I2M21VJDERDFNg1t8nXgYV1By2RUMbPpPM3cSebT6YEHl7mLZ07pXLgzwopgR3XJN1m0nbWb0xaPZdUOrtDm5yWpK2rk/vCAONiPO7IaoYwj2cB0xmIuZSJnAbwja+GvGELhg0icT8fGDVFp/Wbn8EU4VkxEvLcK7FpiQsTUe1Dmdq+FKKiwY2UVvA8kMRZxDYGE5wdffsrL+UAcPWFwSNLGiZq1COwSCSrZ41MiZeUTOfPMD3wPz8N8hjEwoLicSAq7m1vDdcSGv/cekt1vnmfm05moibv7V7Yd5XQ+kzPsA3x485RPPvlkAVSxsH7BQNjq2pZiWtgIVIqNUCJ33wDY7EyO9vna2K94xepz/V3v4ccP9/5xmLL+hoj8VjFT3i/bvgv8UbfPH5dtF01E/pqI/LqI/DoPU7/9tUJg1TYmRA8YtnNdIb9QTV2nr82WSuYiCqN4RlwpoGsCQqRM0Lrioi2PZDl/zaEwxqwspirfTydezSdmUStjGTxhNzYSmJ4Rq9LpXXcZ2/4xRuZ5bqbOYgL5gutYtKY4vwLLvB+6c675O2FtXuWcOZ/PbWU9HA7tc2WI2npR6jPM89xpQsVcKWZNjYFwzpGDI3oDN2MRslGUj159ziudmAYhCczOIjmTg+yMd2QWZRIlirmDccorn/jCR+JYygEED4NHgyP7tSDHSTNPM0o8T5xOE8eHA3ku3qeszRu0dyNPwo6QwamrhgC1DEJ7B0qXZKbLgpc3Xje2a/83nJLXTI7u89sAPL+psPhbwF8Efgn4U+A/+FFPoKr/iar+sqr+Mrdje7ALO0v85YPmauNdxlNcTLCCbG/dfKuOrKqjgsvGgeCy8syPhGNkX/IqGnV8iUJMKTXPR51swXlQR5wzc8w8nCcO08xZlS91Zhod0yDcxzMzmZjnMjntJ1UBU9D7frLVZ6gTcZ5nTqcTh8OBFBXJYoO69E1W4TRlUlKSYiCy89wfTlbZPNd0dE8II4MbcFpC1hXSlJhPM8f7I4fDgY8//phPP/20CYEYYztPjskiOcXCsrVgD1XI1HgOLdqLH0IT7AMOnaNNnJTx0UyTP/jkT5hGx+wzXrUA1Mqg9tlpZhArM+ABSQmXM/d55tN0ZA6OYz4zSWQWNaEhwvF85ng8tj6epomUIjHOpJRLaYSB3XgDWUhzZpom7u8P3Ergp25f8MLvbJLXsQhNO+6xmpXXqA65Msal23fdrkzL1wgCG7yPLLJ1+1vALr6RsFDVj1U1qQUO/KeYqQHwEfC9btefKdu+vr1Om2hBTx1HZkOZ18FQm/u8VNuqeaKYOaG2QrRjc4YCbO7w7MQjMROjTTpwRuyas4UCc8UbU4C2XFaupJnj6cQxzUxkklgmYx+tWX9XE6PS89mAWzg1lliGhZk6zssqvnavWl+dpoiIJZMtdPdrvtLKKVH7IKVUgEDb9nA6cjweeXh4aMLxGqjcp5YvuSvavC0ZFkFb3xNVKJaM32ifD3leNIlqfqEgxrG+vONUQFtFnBJFOeXIkYR4T8y5eHEKHUDRDDVlNCbm88R8nuyzWgHsFE0YzufI6WgZwzjH090tT/a3PNs/sfiQWlipPc26X13393ZsXjVDyvNcxSDWg2xzTDWr9fo+b6F9I2EhIj/VffxXgOop+R+AvyoiOxH5eeAXgF97o5N+DXJrtv9j4M7rCHCuUYq5ThK7JvVVjUMSEQYv7MNAQIy+rly7z6kw//9GQOUFd6hktNkJkybOKZIKeJ00m3utwyrqRE+dkLgWXl3PX122dZWvQqICoOblWBPral7MBFhMn3pOcjWzlvBvTZnPPv+cL+9ecXd4WIWabwVFSuYRaYIkLYKhfl8nbt0nebGCxmqCVMvnz+cjD0E5FQGgvporJuujw0oWoswOUnBmrkjiXme+ytE8LEVw1metiXDpPBHPE+k8MU+TsX0XGRZz4u5w4HQ8k+bEsNvhfUCy8nx3w/vPni+jqa1HBW7V6wKjCrbXCgLRx7WEa3hFf67+u+3xb0FwfC3AKSL/NfArwLdE5I+Bfxf4FRH5Jeyd/SPgX7P71d8Wkf8O+B0gAn/9jTwhdqELAKeqaa2pFjq4RQBcF7rd4HdldnaxG7aPrbqScruOU5CYGUbh2fiEn3z+IfskDD6QzkallnCcDyd2PrDDMbsZJ2JkL0VhSWqpyzFlZlXOKXF3PvEgExNK0EwsK6MFCpnrL0rEYazVMzQuDMPQenOk530UpjgTzrOFM6vihxtUjeDGOcc4jkXVtkEfYySoFAymeEtibCCry4EkIGJC6HA48I/++COQXIBT8ClZIFgBhn2ArJGYwCXDH1IajCpPhJhmnBSODu/IkpmmU8F9RiQnppJmfuMD83ziQRK6G4unpZLiaGPVSmLRsl4ECsCbsuKycE6Zj04Hvpc974fAmKPdcxiZY8bhiPNUQvsx5qt5JsgIGhgHz4cf7K3CWc58Nh+Ic+azjz7hJ3/h5/mJpx/AF39EC4bKtURkTTKrLs2KgWmRKgV0VFkYtOo9LOj5aswvf5d9RS5LdsoSe2Rzqfv7LbU38Yb8q1c2/2ev2f9vAn/zx7mp1sQ3Cjqll8JrCWGraaEmu9heD1l/34Co8tmVlVjJeL/jyc0Ng/Ok84QrdGxevO0YZjQJx8OZmyAE71FvkqKt6EgzZ2NOnKYJHdUGiJNWetFqChuuYiqygCvjyl0+p0ULrrkmDMNIuDL5pTvO+6JpOG0YgnkCfIk6XPrEFebyVPtClSkaFX/lnJxTKjU4atyEZemGUkpIoUVrkiOJor14cwsaSc9S+9Uh6Bzx2fAisN/T8bh69qCCUzFPVQay4Mt8tLySIkgUgnocysM8MfnMSROnnNkhJHGcUXxW9sPY6AMsZ67T8MRwHpJpaWaGeObzRJpndsNQOEWlUBAuTa9M0kuzo3ik+t22uR3VXF6faDGlu8/t72WwrH+/hfbjeEPecnMXP6qPh8XWdhmstGlFtX7T7TXh6OnTpwiQ5sj5fOZ8npsN/uTJs5ZLsKjWCxjZT+KcC2iZl+pe9fttRmv/90WWqa6P7Y/J2ep/igg1uFWcmh7kXBMeSrL09Q58a2h9vefuvEkN2JvnmYS5Leuz5JJ3mVjcuvW8c1pCw2OcOmrCJWu1v6aKMOe5BZClFHk4HZmdsWhX7EeL5yLXYescFb2o8SK4ApyK42Ge+ep45H6aOGXlmGYOcSYqpcaIR31AfWBWiEhZuBeyoFozpb2LBA93BwM/oRVzehSD2I65a3+LLObHav8F+DbzJRXvXbdtG8hYTJitV/Fr58kbtHcj3FtYbLn2kAWwwV/xDy8RjSra9XOVuJ3NWAdRPV/Wpn5Xgliz3211nzx8V274jtywE4fsBiQr9w9H9HggjAPqrVDPs/eeERVSTGSXCDhIkcPOM3sgRlzOnPOZQ5iIux2zZHYnCxhKmhHnCVgMR5LiySMTfVlBrwgJITMEU5vjNJNj4tOXL/mJ73yHZ+OLFm8heOZsK1guE9a5wDgknCQoYebeD6goc0p4TGsJ48Bpihxj5suHI+dpRsaAiOPu8IAfB/aY7a/eGKrCnNEELgizwkGM38KhPPU3pfI8uJJfloWygmfyaWbymWFQ5unMF4d70uDRDDfeWUSmCGgihxJYVjg/Un3jBVeag2lX83zi/54jP/vihp84R56QuZkTw/tWimHCAtX8DMNDRILnVbq3sgkikJVzEWy3gOKQ5ze8fPUlt8+fwVw8HCIQLF8l5WRMZUoRYyVqWBQrEZ86wZCL5rAQ45gAUNOCNVAFQRPj4kwjUYoZkxdws2kYRUNp8uFHJ7W+1t4NYVGfY6tOVdur36aP8AU8duoNDmJu1E7Y1PMJDegcBuNe9EgxE1iR1dTw7xgjw/4Gya6o6YstmYt6P8WZcxcaXWP3naxBsP5+Tc0Hyqq10lS7FTyltMQLcLl6rEh/WWspCXO1VhbuWl6wpnPHGFuRoOPxyEwmOBOwx3niec4ktZU1+2KTF/PI4Vf3CDRvSL3P1WrcJeullHBqVHe2r7aYk9ofVU+61uzcZShJ4pXO/PD4isOz73D0RRfKM4MP+JyJgHOKiqXVO3zDEkSW2ihke++DDy2upmmGZXHrtb+r49BVDOM1E7did1fbI4aAVixkc7237A15N4QFHSgJGxUtF9uuvEDnLsBKO2Q5ZjUQm6AwKS9OGtK/gKS2n6qyy44Pnz7HJSXg8E5IJZNz8MbrWNPLh92IC4JjIOdoA0+xUO/zRMrKYTpzTInJu1KExkKqB1dS350QfMC7dbh3kV71gdozVS/GnGLBu2xC3t/fW7bnE1bRifV8WXMb3DFGGCygSEtUpvemJTQaQYTD6cTd3R13d3d8/4uPiZoYxPFP/uR3ea4TT/2uYD2GU4gK4jI5hha0Vr02p+nMiJH4Osm4yglSBJeTognFzJMQeDgdbXJ51z1DySrJa8y89mOLZdACC4twPyiH45d8pA/sZccLEVyekDnx/tMXnCSTbgKff/45T4Mw3uya29gYwGwa7vCIBvaScClyPk+NRrAKE3zlF613tkkxKO9yO7FXgHzbp7YaAl76IW+FzbVj6uW1aVtvo70zwqKt8LCWrmqrAfirErse+1hbmI1YFS9SXadW17bzgdswMkQrE1g1gKG44HDG6wiFFi4rUoKMNNokqzZ51Mw5JSZNJF8mfOHtXBHzPvI++7iKVuJww/NY73+aplXyU+2XrS2taiHfXoVcvC0NtyjCJKnFV0zT1GIr7tNEUkukeohHzimC362vV95XZSrPKL7gDP0zLfe0mFbeW9GglExTmqapYr0FoF0mSUIXd2XHISdiXrKMaQXiBDcbkc3LV1/yU+894XkIxOOZ5AJBHIMNggU4DSUUGyzxrF7HCUN2DJPlzsS4RB1vF6rcQOMi/LP2jpGrbev1u7ZvW0T69iaY3dbD8g3bOwRw0iYwsEZzRTBK9SXE+PXnubLy0HlANoBUL0R2eAZxhr4XtL4/RwjBtIrC5VhtW6hp5JCTrYCW6TlxTDPql4ropv0vE73XGKxdBjz199D3VwX2qgqfhRXnRDNbZDk+58xc4jL6AEQwt28FTasW8uXdKyZPY9h+iBOnNLd4ih6sbQl1bqkG3zJZ3aIhRY1rk0q1BbKp0ng6M9o0sr5lodQekRWXRS5/m8NErQq8Uz69/4qvjvfMZM7RANvJWdh9zpnBh1bC0TmHC75lw9a6rX4YGMfRfsKwCLo2arV9vjbmHjcvNm0DTvbn6ceI7dNN4ce0i7fU3hnNom91xa8DC2iRlZm8mmjAer/+PCLkprIupkYqAKnhTiVpqpCrvO927JNwG0bG2Qr62vWXzEwrXDyQMhAT4gX1g5kg4pAJmBOfffUlX01HzqiV5HNCKvflh4CWZSulZJmt3rXfDoduaplWLaMxewuN0xPnSgp6wvsBoWgIpaaFqmWehiKITvOEG8ZWmSyL9bFq4a7IiYfDgbvDA3f395ye5RLtkDnEiWM6E/WWYHLcSgsEjwQzb8ZxhxusWnoWcMNSUR2Wwa+A9wFYChyd4sx5nhDvSEUTSmlZbSvhTS0iDMaeBcmYOrxpb7lw16oT/vTwBb92f+T+J4/8pe/9AoeQ+TQfuB1vGJPn6fNnqFjAVs32ra5cUYwVPQk3T5/g4rwUeS4go43BDKSWbbryWjln4+VK+5G8KMDVNf6aoKi4xRWF5Ju0d0ezWNnrm1VUjcijB8ba9138/botFHQX5+o0i/67EAIfPnvB6ANelzCHpZCPBURpRRS9sSxRCjiHQq/vS3LWnK0eRlu9K/OUWqyD7zJhK7g4lwkfO81g2x99mLj3nnNJpe7BxMqRmXNubtiWZl3ON82z8U6wRKZ6741ZXB0PpyPTbK7W1FRpW8n76mVtwhaTxgWPeGk1WKvrUVWZm1dmScfOOeN9IBZOULt/XblxSesYhOpyrWnz1haTLotN8CRaiHwS95L4o7vPiWbblAzRxBgGnoz7coZS7rHEnIxhWMoZuOWZht1oQjublpK7kPy+9W50und51aSuGFuPtW1aO7bHIqrWsj2maTOPn+9Hae+OZtF13oX9xnWEuR2nV0Ci1T7Fri3jsw9EgiIU1FaQ23FHUIGYyZGmSntvKl8rIPuHAAAgAElEQVQNH85aEs5mU6ddyuAGKq1aRpnJxkLtXUHcTZvx3q0mkLPSZK3lbGnRYWOmXDND+r5pA6nDObYxGbU4khb3XJ30wfsmkNESTxEtHX1Ka+/RnBJTAXoz1fyoYP9SFiGzVJIH04IqqLmYm1bkOOeF7Cd0WE3G8j3qCm6nKttX2uVS5qkJMcmcR4fTjN8NHMl8FO/55OFLduMLNEVwASSBdwiesRAc96ZP7VMj2VIkeMs6dQtw2QuAvJrH0lH/rfkqlj7l9ZO5M2EWbha3fPcYJiHYTb4lneAdERam4D6qXj1iu9uLKIzQBkmtbEMrPLMJWPFqbmgAXEPzRWFwnn0SbvzA7ei4cVjKdEoMw41VKR9Cc48mV6pzK0xitTpBOcWZu+nE57NxV8xhCVFGYPSB2/2NCYGYyGLCxCIsSn84QdOS/9GDndW9WTWJPrU854zzaxxEVZFQ0tWlo/9LmUhsDFKV88OiMw2/mFJstrwKZuO7hdKvCbFsLFY433J4XAE3k2YoHhcni5u0cWTiEUnGaZGVLIkpzot2VN8dFbS112mcFh2jV2muuiidcvCeMGcGl3gIwpTP/O/f/23+8k/+47y4fcowDJx1ZhwG3DgwyEDMGVe1pRLxavwcym7co9FznidzP5ew++qaThaN0e6lllPQYoZq7qJm2zh23d1vsDrVMrTd+vtqW7QQ8jW2tvIEvqX27pghwDYabeUdKe1NgL9V3AKvtwmbOYJ1d/C+ZRKKLoBmv1LXCWUTOaxIfXPOTJo4zmfLMPWWSFY1DscSFVjP2RfuAZoJsvbjL61VL+uAtF6Y1PMCF0Bnr43knBvJzlZzMaAWmxQptehJxJNUmFJcgMSczXPkTH0HVoBqNff6z9v07f66wDortVPNLwhxV0Kk/HTdNWQIGK2ez47ReV7NJ/6fH/4RJ50tsU/V8lzmiAzBuDkxcyoUTaMS+6zA3A2HR09gZC/qyjis73OrST82ubcmx+q77u+3LBiutXdEs7je2iB6RDBUr4dWyS1r7aTZtZtzUMwByQW8UtMwgjhGsYjKIA7njGFa8ex2u1b+LxdVVLwjiKnDWc+knJmmma+OD3z02aecvDJ5Cy0OsajeYjEbN/t9WdEX4pu66koQCA43dWHSzcZeJpiIkObIfJ4WQbYZV/1AtsmUmSXhfTGXsgHGFT+xlVxI2eIfpmmyNcw5SxXHMj1nMqlQx1XB5AytaZPofD5jgOzCXRFClQZdASRZCxQ/hIbzNIFYqAdXtH2Fgas3v/r+csDNbFmpBE9IGZ+Ur24yvzm95O53fpVf+u4/xl/6qZ/n6dOn3D59xpef3RFC4Pb21vrDZ3beM8XI6EfOJHZhx3meSkGnGh9TA8Zq9Tk18PzCpK4//UuiEx5dZT3M9AIpprSjatDdSSkD6OK9v23x8Y4Ii80Irx3caxavkZyP2fKv2/8RWd0K0UiuqrRDS2yD1QQp6nObhNoAzsoANSdzmVZO1W1OSPC+aSP9fWxVyCyLG64W6xGWVbSP0OwZxh/Tuuzv9ZP3pkpKqXmaWm5HTTd30mIyklopxXmeyb5bZcMCQNdz1utXdb5/xqY9lGJE3oHLlu/QqADAiG+2reAu7fk2uM/S35C9MXANKoxu4E5nZAd/ePgC94PvcxuFD5+/D35kf3vDPM8czydub28ZxFjFhmLqBDUMap7nRcMoIKrYA1ifynIPq/FY38EVXO5iQFy0R0z17joF0jF+kuY1fDss3++IsIC1v7j86idP11Q8LSNAapDUpfqnCZw6MkWFKPtJBpdsoE0uE4D97Hjv6XP2fsdOrVvOHm6HHUOpRF5t05rjoMUXNjuYkhKS4GLm84c7Xs4HTjvHnDPOeUJSzmMmD57dsOdWPQOBs4PsHT5nYEbFG8A6W6pWMpjFhEchrunNCRXwQyApDMOu5SR4h2WhYniEF2PmrsempIjPFhEoVg4gSAn99g6KkIhzLiTA0bo1KykEZrLFpJR6HzFkTvGMk5HnbleiB4t2oNmKDsWEOm+lP4sGkZLlUuSp5GmIw0so1cbyYlaIaTfETl0P5soVkZJyURnYTajHnDntMNazKKaZJWFvBQc4a+KHd1/w67/3O/zyhz/Lz77/E/D+C0SEOWYOxzNJTIv0w4hT5clu4LPPPuX//fu/TfIGopomYHlHTqxsJbrUE1nGdb33TmhXraLkc5iiWXCZrhq6aNFgSgHlpkU25bsXPiUDWGkY1dswU94hYXHZtE+OKa2pV3V7yg2Nhq1gEVzWBZ2uASrVbFEQZzatF8fT/Y1NLKwwT3Wf1ZXdfgQ3hHatGCcDI6fE+TxzOB356LOPuc9nUvYtQlPHmqKt7HYWJl0Jba2wTs2WrJ4Wy9eQAjzWIsx9iYAKAKqWKuZDoHFKdthEz2q12PkFQymr4zzPVnHdB8PNOk0FCvbhbHVt5/KmgdVApt5NWo81vEMZhrGdp/ZdH/g1FODS7rVk8bYQdXtpK6yqGxem9YXV5/rsZ2/xOVOwwS4qkKw/7nPiPChfceTf/LX/nveePuNf/qW/zM9/7+f49vsf8O3vfoBTA5LPHn549xn/xf/43/IHn/wxr6YjfGvXlqhUeCxy6dOK51y03iwW6UyQOn6ryVU2NS3NFSC0EzZtP7mwbGp7GxpFbe+OsPgaU8N2udTRrm7T2kn1xQAlCaqCTEZUsxQp2vnALgyQauhxRa4NxW62f0elb+wXhn4bBZtxO541oYOtoFZwJhOxGIYsylhseLPni0mSaZGMwdXArDLhkZVA7FXbuq1GlEJR8fM6eG27v6qiOZvGxGLKLCj6AqyZql36FpZ6ITmTs6n64zAwhLGAgNV8K1GwRZDkaEIg1urrWqM/16ZS7Yf1O17uBVk/R98uJkda+rAOB1+qMaszTo9TgFeS+dPz54x/73/jW//wt/juB9/mn/2L/xQ3YgWsf/P7v8vv/fAP+fuv/pTjCPPzsMJIVuHcrpi5rxvP0o3NCwzDrz8XFvEVENyE5uOXsMvIcpkfs707wgIuBcZjWETZXDNC63dt1ewHmmJ08awnS7Px1CL19rsdow8M6huiL6W4jnPG7qRS2JCE5maknFmT2fCH6cxBI5N35oJzVgYweQgF/KrZnLZqOshKKOUAKGnpqtpqjW5f9DacO1XNovfKaG4aWI/Y931VEXoL8V73c2WGy2KC0ejrldwV/a1uzSCXMR0550XIdinSzb3bZ8SqCVJKqYWHeG/BY0P93sCf5dI1SK4uvpY12o+Ner1dLKZQ4ZlxapqkxcgK6uAYIzoK3g189MUXvHx4xR+//Jjv//7vEdQC7z4/P3CvE9NeiU7IXhsWheQFMukJiy6ERdU1ruEOLQhoI0jK2O23903bHo+2t+VGfXeExZWVr7ZeKLTPFcxZnWLh0mzIs5QXoIsQ1qLyWjo1aEp85yde8MwHQrKJ771nHELhY1jCd7MqdVwiZrOej0fO5yOn84GXd59zHpVpECQv6eqTgMTMzTDwwdOn5FPmcDhY4pIqxzlZEJcqQ9ghIrx4ctuiQuvzunIPKVsMxOF0YpomduO4cvM5t6So16xZxfJWADKJYRiaZhRCWNX8jJv3kQr1nwPzMHlnMQVuqeJVtZVYzYCS1i3BSHSqllGBzSpgknNoYzVXHg4H5nlGxm4slLB02Zoj3Tu91ubifZk9Nh5IBLEiUMFQIQRlxKNJeflesLedJ/6RRMMh1BGeOlKCKFhtVu+Z/TrvJkMpgdm13uxobS00ylPR8j3aoli2LcCE7dtkhmtAL1KZtx5bYL9GBXmD9u4Ii9K2rqaWNaq6EiS61RR0dZL1OWX5Xrtt9fggwtNxz94PJaloiVMw114xPeqKXDWKQm5b2aROcebhfOKMBTvtaiHgUmXNOxgHz+24w51nKCHRu3GEIixqnY4YE9O02P+umEs07MStMIzWLytc4kqTqiJTgstYHZtSMq9OVwdjPRFdwS0KsU1RQbz3eBynKZK1hHSH5VgHzNnqq4oLq3ttLlTnyIWaf5om9KYAh8V7oBVr2j6bCNoVeu5bDpf2fHRAzkRv8SFePHmaTaAGE67mxcqtcqAX0GQayRyjaYmltEA/rnKvAVzcZ6FbuHqjSi0h2W63N1Hqxn4st9WvbBQji7ra/lxpFrU9smKs7PTuc9UmVq29pKXjm5tto5EM4rjxgQ/2T9hli7PI0SaMH8xEUM/FBKxUevPRanDMZL44PfDp8Q55ZnZ7o4/zoJrZZeEnbp/jo6VYp5yRlKBoMjFbfMA4jowiRWhEbm9vAVoKep+K7r1nTpHnw03j2+yDvHo3KFi1dRM21RRaBldf6VzE6AVXQlo6vAIDONOcSfPE8csvGVzADwP7EsSW+346n40TZD/a55zId3dmkt08Kbwc2uqLtIAn5yBhCW8qeFHixWRp9sjm/S/NeDsLe7vasQmLw4kojIEJuEkLpWO7h4wl/TmLsA2jBW3l1fW/ZjKKB+LVe2zCWO0epYzXa67SC9Bfiu3ahEmXQ/OYYPqG7d0TFo+0S3CnCA69/L629v62OEaxDYPzSLKgmyGqFaxpUZqJnH1TrV1vDuRcaOMTOVr19dM88fnhjlmsqrcUMydDYZQqgmkYCbmskH4pTDTFJeXbqaVXVwxinmdGHwoxryWChRBWMRG956I9Z4fPNNR+E0vRR6U2+x8Lpa5JU/W5c7T9c4/XFM1nt9sRxFimqplR2cVFrGRhSonz+YwPgWFvXBjnacL5AZ8d2cN5OgMszGOymS592YevUa37VTq5Hn+02iwWkboOZMuJpsW5DuvJIs17YQFj2wn7CKZw/cbW91jpCbamtVhPtDQGrmATFyS/Cwhdz/m22jstLF4rrFsnXAN9DKPQXvrXF1paTVjyWdiPAzInBlFSToTS+Yt63CU/FVW9poanlNCc+ep04IvTA2enZaFZbP0MuAzPb57wbHdjGa1YcM+w2yPqUJ2a14BoySs1hDrIouqjyv3DQ1fAZw1e1nTpKgB8oeKv6n6daJYxubgYocvGLV3YeCguBPVlq5muIoIbhpZlWb09IQS8G+y+Y8SlgMsecY5pmhhlIOdFuG1rpdT+r2Zpr+Vp1Sy2AHkdC3WiOcttSWV/KbE2xs5tAi6ysKlpIb3SypmpJTiteW9+xImoHf7wJk2W63TrY/kOGvaxAT+1jH9cXqTGnx8zpKpc5e+ryO/abn48BrMHjAxkw8tScTunAm5ZLsDz3XP+wrd/iqca2CdbTVr1qzRxEmEYBoZhbN4DjYk8R1KJ9NOU+cEXn/AZE5OFE+AVknPkUFbJc2ZQ5VtPnjIkwYdAHoyX4pwiUbNlpWaFQr1/LLU2g9iqHMpKPQwDx+OxreBNKNSw65Y3shRY7mMPatnBmttiq9tCMiPdhLy5uVn6VaRFq4ozb48E46WcimANfiy8H2tOh9NxMnxmt1sJuHqdKc7MRPCemDvXKlVQrAmL2oS4ePnrVdvut2SCtgClJV2cbGxevmh7ufCEJsx0TFT3bvndBVBMbjNZe41nq/30a5UqK/fo6p47jKjXjItWc3XSF03WDtHVNq2mzJ9HgBNYPTxcrmZXH7vr4JUALiq4qrbydIKt2uI8T29vuR12hKNCjpxZgCYnikuWSNbuoZyj1gmtlcDupxPRYYljmCmhhcUplRve+WAp8CdHLdk3ZUWPZ87xDN4x+MCuvNhxHEtR3kVQVG/HOI48nM4tKa13ReZowV41AW7bhzFGxC8reEqJISx5HX2/V09HzkWYyRIfEkKwyuYpEcv9VlaplmBWPmtcg6WunCMVIZNSJDvLu6l9ur1vkQWsbEKtLpyPLDJa4mtcUz6khF5YqL66Si9YhpCmpmnV2AmrhleSAKvmplzXZK6Ny5WZsBEUq5avn04reNl7S9aZq6s5Iptj30qUxbsmLDppXCf4o/u0j0viDSz9dHFsBUOz2dgpJm7cwM1uX9Kzy4By0kyYmCJME957bm5Cwyuq0AHjihSxLExGQYMgOePVWLFsTAkivk1qs+E9eQiM4vF+wE9GOqO5xOi1NG/X3KdQWManaeH5rExZLGZbxSUqNrE1VSroqKrEkgavu8Go5Sr2oUv8g3Ou8DEs569CRsuMqs82xZk0z0ZekzM3NzeM48huv2tmXUqJNBkTVhiG8soXTeOax0MuJlxNHqtaxiMTQhbhLZ3AqLy32QnqSp/nanZQNNc6Hs3BnCjZxv1AK9d4k7bydPyoTRVIRdA8Qrm13b/Nix9fq4B3RlgIZE8rdpu1xfw3dLi3m+3V2pEdkFNNE+3D6XKJflSHR4kF7d/j0QTfuX3Oi0mQCIedMM6pgXreC0/3N3jnSSs2qgySSdliB768e8UhKA/OZtSu5QUYduGS4gfPSMAl4UxmdMKYjAXfjQNhSniCUfSJJZ8FT4nkFEaswO+cIzklbsYdn3PHpDPOW9XxOhHrpJzi3Posx4XJKedMCA5fXMXeuUay67UwXYkwCAwkRmgh5OrEqpLlzOA9MlUNKRPnxLjfMez2BFUTGvPMV198YUCweJz3fOtb38KPQ8vhsEpsmSkphKHxaNqNm5s8qaWn51IJnjomqkogi+ahsriSyTVlvnJelB8tXgVNJYfIBpOWsdRwEDDVsBQ7yapNw6LHVTohuzZBanSbArGYQgtjvZbnWkwPXY6puSSyuGhbiLeWObAgmcu9KLTEHN1892O0d0RYLG0RChaoI26xk6+5Ubfg23YFctfi29TU4NEHboYdQ3KMIaBO8H4hk8H5Va2QKixEBE25TcCpTDRcVVlt0FWC3gXjXipxGXjeZZSWCWpgmxZil17dFjO1u77wXhAPWfLKXVqB13rvNRCq759MIIy7pknM84ymbKaLd4UXc2EKq94BgMELYXCNkTtT3L3DjmE3tmjW+XxutTbGQlunqtzd3eHHgdvbW8I4IL5OPqtYBlwAnPVYS//2bSI3TeSbUN6X1bfFsHTX1dXKvN5/5Y1ZTfLXXOe1t7H5XjYC4CqGV3eVtsu18/Zz5cdt746wUIsQ3AKZICUSVhfh2ndC7cjcv8iO30AigiI5WTQeSgDGBC/GvVXROitBAlkt7qDnX9Bk/v84zc3NaRPR3G/neWJOkUgJZkLJ4hv9mYrpS3P1RGhicDu0MEdV9f32dk8sxYudGEnL4MRCirMyT+ZaTWLh1TklztEA1tM8sduZK7KZOSG0dHvvPYfDofFL1H3OJfbBSWhlDaZpMqJfK4e2BKdpLtXfhHG8YRgG5vnMkGyyxtPZ+EFEGEPg4XRinmd2w8g5JoYSuu2c4zsffMButyNqxvuBMzCEgVmMOGhxE6YFyC6gZx8rc20SVAKayvbdj6+rE06XYK++NZNhC1SyTFAp2xsQe+XclwvVFSG0vnC3b/282bcTUAtWc/10b0tQwBswZYnI90Tk74rI74jIb4vIv162fyAi/5OI/F75/X7ZLiLyH4nI74vIb4nIP/NNb27lHuv+Qe2ENWXeYpdfcmxKrpyNlkq88wPvP3nGQGDA4dQi+QbnCeLKjy/s2AVfSoZ51B9XVN6E1TFtVHMFHGs5Cd1zVDyh5odUME8KIHlzc9OIdnpKvVbhvIuLyGq1SRK62r+uwPM8t9ofp9OpmSgxRu7v75sAqdwM1c1ZiYN7uj5UG2ENmIY81IQ4tX1Op2M7bhzHlqQ3jnu89+z3e548ecJcIjRHP0LKXUbv4iIex7G+1lX/ebM3VjiM9Or+n1ETrk/wFQD7Jud5HRZzrfVzvZ/4TZDwuOCBrn9+/PYmPRyBf0NVfxH454G/LiK/CPzbwN9R1V8A/k75DPAvAr9Qfv4a8Ld+pDvaRJ01BP11D/yI9Gw5JMUu7KntBnE8u7lt6elaeBly4byoOQ91Ai00dQuqX0G+mNMqLVzFfPlV8+nvo2aD9up9nysRQsAPoYGKzfPgSv0K51sOR2PuBsMPZAlT74HRakaZC3jgdrdvLswYI6fTqQmtKrj6RDXXuaa2pmC9jwraVuHhMJfzfn/L8+fP+fDDD3ny5Am3t7c8PDzw5ZevmqCqldCGULNxlRDWiWb99WtZyUeHg7g3mkjA62N5mnYqy776+tyU113tmvZyZac17gELLnPtnr5GGLwtQQFvYIao6p8Cf1r+vhORfwh8F/grwK+U3f4r4H8B/q2y/W+r9cz/KSLvichPlfM8chEWddOVXIBrrdkhsHIdlc6tk9NU1YhiGZ8NMRAMnErCrQt8sHvCTj0B5Xa35ySeMVgMR6Xb83W1K8QwFllo5pGqcphmXr56Be95nF/SzFUowschWO2L6rWYc4IMYRga/0NKqanWuXBSSKn54bC4Bvn/2nvXUMu27DDvG3Outfc5VXVv39tqpWm3WugZB4UkUiOEjI1/WCSW+49sCKHzwxZBoJDIYEHyQ7YhKBBDHCIJAsGmjQyOEZGVSMEi2GDZlhA2iqSW0upu9UPdUt/W7dv3XbduVZ1z9l5rzTnyY8w511xrr73PPlV1q07EGXDO3ns95hprPsYc76GJa+kHCBaT8vDinIuh4/T27WJa7TorJ5DdxHNEan6+cSIt67U5SnnvU/nElOm7aVKtjlCIVQjBkuJkApHeaYVZaFSEO3ee49ad2xCt2LL3Fuey2WysD73HuzYVaRo5CHdyQqtCTFXTwWqcmBhnCsFiLYmVr0WZFnnB1NaRCE6SN2yRXyfTyaXrYZcIluuLTqC6N98zdxwjK1nHtoraQca2s4gzaXMi7sgOriOHMb1Hs6J2cu0o/kzei8eDK+ksRORbgO8BfhP4YEUAXgM+mL5/GHi5uu1r6diEWIjIj2KcBzw/Ov7UL7cfqkE6IJNpLlwjoMmZRv3IQjfiaKJ5aDrXWO6JYHJ0ZroyexxCJkKxJLCVlDGq18gQFVWzxGeTnllytGjqN33HdgiwqhVTafd2Yx0RlZQIRilOf85pSbDbp9iJnAE7Wy9y39XiQyYUmZOJ0aJdvZr/yMSRS8bM4a139A8uivIvT7bMdYSUtUvEiFwdIn92dsZmsymm09PTU+7cuYNg3M16vbZ3boxD6vuetllZ8FgiRJmbnIoZy2OddQbV5MICqhInqRQRsZ4vSybaKVGYKTczlKDRhUW9B6+cP+UoYeUyPUN9emmdJILzJLkKuAKxEJE7wC8CP66q9yf6BFUVOcjQ7YCqfgL4BIB86AXTJVWRg0cpZsoOMPtdX7KnrZOmpaWx4KR8TVTyYveJu2gsgw0iK4JacoTt0Jcd93zoLHmtWn4DL1I8SFVHogHQ9anIjxNUp/klMsxrTuTEMBJTQhnALEUpu1W0gjx1bEjJjCUYIXGu4JB1GaVQUJRi6cmZqZyz8n3ZnJpxyYstt2GemrFU7LKdX4qCFSiij1CJSc76KCt3tyGiYRSBSgEjwfoquEmfxFLeYTyuaTfVPOgLesElODjP5uLvIXF4PpZL1+YdXxLHk0ZT3J5dv+BWtRWr79XYaDyAG0eup0vgKGIhIi1GKH5OVX8pHX49ixci8iHgjXT8FeAj1e3flI4dAIVcIXt85t4XXDy+RChUEXWIRou/SO6dLY7n13c4xeODafhDb1XQ2+hQzLmqtih0YQCsJoYPDUO/ZTt0vHHxgE0DcZXimDHW09KgWT4tnDCoJfI977Ypy5ZPi8bjnSlgFYr87pwzicyb5ynRclHEGDnfbrjYbnh3s+EiRMvTWeXm1LSwsweniHDRbWy3HwInJye4xkSAUQxxDIMl1TWxAl544QXOLi5K0mDFJrhZYbYMEU4TroMqfRggWvunp6coSfRoGqxIshGKzOFksQjfst1uaW+1bDtTxPZhzGw+fhqRPTT22fRsBHu8tnAXV4WJeDCFklKzImxTXYPuiDnj93ROBK2rwlf6kRwpXYtb+Z3q9XGpTu8JwTHWEAF+Fvi8qv50deqXgR9O338Y+KfV8b+WrCLfD7x7UF+xCBFL83+Ep9oByIOpOlowssJx1TQ0KqlSepIly6YwHdgY42TAXIofGIaBPlqF9JB8YArH5eo6EmY+7YbBHKV0jCIsYeyyW0OksN/OI2I1Q3N2piEEtn1nhMfJ5LlFbEi4Z78LMA7AOcdqteJkfauIIrWlJJc5NKvMbtbwfF2MsSzA1WpdCJZvGppURNjex48KV++K16lxV74oSDOx64JZY9DKR0VmyjzVyfyYEpVxzOvz9e85H5w5yyXl5fi86nulIyuwUIR4UayZP2PWzqFQh8uO122qHnifR4BjOIs/C/xV4DMi8ql07G8B/yPwCyLyI8BXgf8snftnwMeALwPnwH9xFYSKcgpK5a8jbpr8rBVJpqi0pCWR5GshQisWjakhoAN0255ttCzYk8xPqe22bQniiIk172PgfLvhXCOD90hrMdAaFOdb8kQOauHmOZFuJjDeSVFIiEgpUGwvkBZGymfJYBmwiZ6YFFoR5fzigs5bwhPXmBIyT5KcNStD/U45T8X9+/dHvwxvbsQlDN4JQ3KQylm2rNqWwWZj3M0tv6JtrQCPE6vWlts0DslEjWa9Ksctk5bj5OSEpmnYhoBuewvFX614+eWXk1K0MbFHkoK3yOFxFDMqNUDRQcwUoHvn2WwOqdqc2wsznUc5NmmHwjHk30URKuM9Oa/qkqhTuJWMSgqGHB9ZiTyF89nFdaYq2P9eR8Ix1pB/s4BKhh9YuF6BH7s6KlWB4xJok2cDe2RAKr3FeL4UHxIAhzbCNvbIYM7/Lgor51kPZq3YdB3PaYsbhNAIPgjNkLwpIzQOfFBc0zB46LsO7ZSmEzanni4GTnqzYgTx9Pn5TiAoTbDaF9EL0jY4onE1qGXtBvP7zpypKhqttEATFW1aLoaQXMcxk2MMnJ84Ng5u0aL9QOMcfbRaqjGk56cFZkQzmXujiWXrlacRu861DnxTuJE4RIJzBAKdWg2UJgpNhL5xPGhNZ+J1jfMO9ZaY2ELxm/4AACAASURBVOOJg9K2DSLefFBiQDfWbuM8q5NTvGtQcQQ8mtLTnWpLF+CdoeO8tTlh3qsmAkp0aKgWTjQSNpkSkzT5Q3LBG61torFwAEZc6ujPPKnSz3qHF6aEIhOO2Zws4kG6VrKooUkOqnVSpbmZu0DhpgLlBh0VzBO9SPb/WFAZLklsjwPXx4MTl/p/mUu4DGoupL5eVEvlsTJoqRq2iFhuyBBQ15o8vW6h8SZaiOI0otKkCEUIETZDz1YDZ8PWSg1KFlfMMSu6ad7WKFNdBIz5Ma026TRc2ZXJleVUynvVokAxw+XJnxWiWRlZ9UXJ3C1iogXmR9KmwkgxXeuqCVyXHaihiGaqxa289Z4Gh3OeftvbPb2ATz4fKwtPXzUtt597HsERfdbvJE4orb1td5EflAeuiCCHRPPCVaT+VpGD7MWSJUTmv/daG5gQip3rrqhH2HnW0vdaLKvRSdncps+uvj8huEbEwuAg6zSR8yY3TaJNa+ouSmXlsBubprGycwK9Rk7WLc6viC4VhXHJKSloCS8PCBIDMQxsNxvOuy1fv/c2/Spd74xIxFpfEDTrO023ECxBcDY/Rl/XIxmzV5fs4aRoSam0/km86brOLCHRs015K9v1GuesdkjURJAqAuO8lf+TyhlM1Y2xKekeiUojylm/NS4mKRtJcTpZVOn7ntjuVnd3KTdle7K2+BURTk9Oiw7DOYeQKtLjk2io9A7a05Y379+Dldm4RSylXoCSaXzivVjNjZIqN2YiM7+kDsLKU2eauq6krZsrD+fiRd2OaikRMddtTBa3amlHF/Gb14StdTTlqtlNO81MiMuThGtHLGqYEA4YqXUcByl/zrslO8SU3Siac1NI6f3BZGfXeMtH6TyuaWiUkqdhCAOx69mEwO3bt9EYkCHiI1xsN9y/OEPXKRtWxQEUfMnsq32v3beL/F0pM8vbVhyWcRNh9BmBpE9IGgQnoBYJ2iazb67sbdxEzr0QZ/TVW9KXvHiT0lZDLIWhiy9HCMYdRU3mPiOGfd8TXMV9ZJft9KCsxMxK2+zklXU0MY1R3/eciHXUpus4326Q0zUQdqpuZavIrp6qmgOprck6PeCjkfVGJr5MuYYl83b10CtzEKkxqM2hRfyeb5QluGFHD1D8kcqBq6HwKHCtiUWGiSJnSW5kdzCtWqFO70/X5wUbU6JcIS0sZ/VEo/OIWpXwPgSCBtYOJNpiiF64v73gQmKJAi7RpkmPsjO5nEW55lByJ67ym1ggFs7yYsx37ZwEtw8pHgVL81bqcTRmfRBMHvfeJTHCpfYjUtLlgUZLAiMJh6wYy8Si7wPDzCiVcRqGgcGbZcNXu2KuF5ItJTFGNn3HSbsaCUsmTmk8SPqkbRwsy7nUxNJ8VoSkh5EKIZ1aasYFPJ9FY0zL7royJfHor8AyAcjcxT6Od/GeA8RkjvPCpleo5ZX1D7Nkv09Dwfl0YGo/n4oioXRkOjm9dU5ExMLHMyeSnXtym06E26e3WDUt9JFbJ6f4IJz4EzqnuLaxyulimbQgWkr41hO2ParwzsVDvvL2a1zc8kQJ4F0pZJRFDMm4Vv4TLqqVRHRtYaVzBiyfStOpjqZQy3MQUFKeiqQf2HQDffKvMOOpR7U21RpEFKq8m2ZhaXBNU8ozZpNt07aIKIEBHaJF1IbI/fMzLvquuM1bpmsjBEMcg+KGYWAzRNZrweORxhtBbOz8arWibUZv0qZtLZN34v66bqBfCff6M869MlQEIZuLc3bxMt7O7Yobmt3xbZHqjAvV+rqZSXiiN1CKLqo2ZU+4s5GVGTnf+bys76g2txqEKV7lszA5df6OkVhm7rlcnwlTzbGUY49PLN7bUL0rwFRWm3EEdsGuvDjnMuaDIbVibLpjqtrCRa2coHPOgrSiBZmd+NYqV2X/jN6K0+gQuHf3HTOBJjNq8YZMxEkS7jkaMweENc7Tel9MaaVSefJZqBc1jArRPGFr78uz83OzQGB6lT6FvJco1rRz51iWjGfGcYhaapRK4j5MkjArRo48fXh+hrRj0t5Y5qFWbY2BcDX+Wa+R36/4eoiU4LqsIHVYXtKz7Ybm1gl9CGRLx4S7UjfK/kOwBDQLClhV3YndmINWfX4MTLiXGSyWo1ias4faXYC5daW0s8S95mflKOHC1ez20aPANeEsdjtsh62cnpwQkcnQJc4iX5cXSO44EaG72BCHAe9PSqEccbaoLObDE+LA0II2LSuxY/0wsG0if3TvDR6eCH3riDLW8MiKMNuBdSRIGGF43+07vHjn+bKbAyXGAh299WLiLuoFmRdVZum3Q8/glME7YojcvXuXk5TCzgl4NWcuV7rJFTY+gqX+c2uS9MyqbdPCSyUK2pZ3zh7wYHtB78Y8GS7Vbc0lC1W1FDYu+hgRnPfm1NW6lPxmxTpxFplwxBiIUelC5KRp6GPgi197iXMJ6Lohl35T1VQoOi+ccaxLdrSd+SHjQjkkNhSOYzTBLlpA8rm04Zf00jXXpjyaubKey/kdxdgoCxTz5i4PJQv5Yhs7ayWCuFnnPDpcG84iw3Sg9lDDJVZuxkGU4zvzROljMMcgl5SBrdW7UO9MpGiMOLBqCF7QxhE0ElzkgsA7YUPfiKX9F1LKtz2zJHlyusZqqOZw+PpdjSDschb2LtMaoriUw1IHI2zeId6sI7Zz73dky5m7sik0Jt0ITDNTqZoic9Nt2Qx9ITA1bjA1Bc89ULNTVuPGPBveWx/U3B1a1UcJA+88uE9sZKwjOhk72cFhPLkwleecwNIOvQCFQ9yzyHbO1e0seHEerQCtuYUZ7po2viu1t0fseVS4NpwFHKboswsnst2yHGnXqY5V0LP+wjJVm6EtOFCnBCeohxXJm9F7HBaq7cXhYsB5x2boLYO3QNCZXK1MfPejjinaVqsVt9bGychs0rvy3oafYSZpblfcRarWNcS+5LKI+Z2qBajRCveKmpVmjBzNTxnNyxmHoBZR0TrHoAHVwKbrTInqxwmbnwcVt+GmLuFzgg3mdxJCQNxIZGI/MAwBTebVbd/x8PzMnNsYCafX1BdulNGnHrYuWXUT4XIL7tRLc2jp+6OATMfzUZdnzpcBiWPxwjxYruhNch9nbkanuo+dN5rpWx4FrgmxqDrIvrDMVZT9bbeFXEMDSaJiWohqg+BToIiKxR68ee8u3/gNt1ivW9R5kBWxEZredAvqzClruFBO1itWbcODswd84eWv0K0cW6dI06A+KWBJIk/Jt6AEsUQtUZX29IQ7p7eJg+IcJVNU4YZiaiNlBQ8Cqk2x6OT+CSGw2WwssM0Jg0RWMibDGTTiE0Esi9Y1RsQ09eFc7yAmlqhCVPPfuNhueHDxgC19Kdg8mWyVY5tz5rq9wirNW27OqX4oNjZ25hXb0/eD5cF1jhgD3q94/etv8sa9u3QSdxgFVU2iXc36++n5NJM0e8GVuVR91lzGElSEw9oca9DkjWnCuc6uB5KfxyXPKvqE1M6MCNpnpKwNx3hMRlyK3LsHrqKTuQyunRhicIky5lAHVDvxRJ6sBm4YBt555x22KR29irHzCsTGEbzQO+idErzgVp4B5Y137/LKm68TW0/nYZvzRbqxfkVRAGZ0RFKIu0WB5g7XpH/IC6r+rrN3mItYdSq+9IKTdy3emkvdwzRNX2R85hjglcymsdIZLLSXnazGrteiq1n6K6HnUILVhmEwLi0Ezs/P7flMZf8srhw98VXLQCx6VtbtVH14kMOYEe0r4XLJtXNxp8zXGU66D+/Fxz65ALIM14SzgKWajXUnS0VAJi4qxTSGsWZVejtUiWLWiG2uMhOFnsir8ZxP3/86P/jt34Keb1ndXkPXcRGV97UN9AOtCs+/+ALN6Qn//Nd+ha+8+SpnjbIVAY20YkVxBAgqRGf7UIugGuicEhkQ4MW25Y5r8UNgcB1OhM3FWdmBnbMANtd4RD2tc2z785R5K9IALkb6bce75xu65GyVFahbHxialFY+DuT4GhcV0cEKIKWdfCXmzeqiMjirKn4yKD45ooWuR4fAO+fnPIiR2HicH6xmqzd2tsXhotCmUHcVMT+VACH0xGjh99CU5L6Na+npWa1Mr9FgwW666Xn13Yd89itf5O3ze7gXVwwhmoIvRswsLDYHFEs3IBVbLrPNpV5glvWGqW9GPdfyXJnum+bkpcapIiljV0rjn84vcbhQ5ROpkkzXStE0cZE46ntUQzINV0Sujn3J3qciI64Sx+OMRHWRgDyuqMV1IhZQsYy7x2aaifFbpQDaARGiz52YjjlJyknPHz94m+3a8cLtF9ncPafFwemas37Ae1PGP3/7Dm+cvcvv3f0a8XZL503HIZgLssCkuriSlIVRoHLTdlJFskbTSYQ4ELDqaCFtpQ7FN/Z9JVbYJiSC6JLzWMhiwcK7W67RXfk0a9hrDkTLvVJqd4RhTPG33VqVtKwEFSIijaX6S2HuLilUsz5IREoOkFzewLiqk1RSUaz0I8blWDUz8xR9cPYwZUjHUvilt5iYa6t3IC0y1MZ1EVSngTr7OIv6/OyYRtnhwbOoOTYzjWEq3BAVd1Pp2nbD0C3vSiVc7H+f8celROBx9Sg1XC9icQDmgwEk1rRckA7NqKsTJEJWLUiSeS+2WxDP5//gi3z0W/80H759h1aFuys4ed8dizLtAq+9+iq//plPcr4W5MQTdcAN0RY4ijaVpyKmJQ9oKtBt17hU66NxvvhVKIJXi9UYAO+EQRUXI41v8eIIYUvO3JXdvXN18alz3i7LuaRkHI9XFgkHGiwIToPtbtk/YhsHXOtLwWPAlI3JstE0DQxmnXEhIL4Z/TuEUmBaE3GxiBCQxJHVe8P5dmNu3o09b+J2XRODwnHKvo199sKxcJ2LoMpy3VFH7eehJP+Ovc3o7ve5CDHBYVdxWXMu5BiVawTXj1jU4sfSANQDto9cymj98Nk/JXvZCahEegcQ+e0/+hyf+txn+cjJ+3j/8+8jtB5un3Lv3j3uvfk255sL5IXb9CeOELeWu3PV0qtl1spuyQOZbbWALU3iQ4wWjn7r9NTKB0RNSlDz0ESE1q2sLKFGhq7n3e09RIQTgqXbdw0hOUmVlHhGPow9dhaWLpm70ZAsQBaEhWpR+ua/KMl3Qw33OEREI6E3v5Ft37GJAwOG15wYtU1TlMftakW7WpET2XRQasLm9HrdxmqWtK7Fewcxmjdp8sJ85bVXrTr8c5kpS+rKmjCIkJxE0uCPLPsEu1rRWIjMjJvIi3Fed1QdO+7kUreVBArdT6yWFKBWTiEd2xF5Ft7jGEJR0y63vJlaLlKmXqKPCNeHWMzt0/s0yIfOJ6g7P6QlHD1FLNCguNbRR+HtvsefCm91b+Hv3uUWnovQg3dEpzQfOKHTDT2KbxsGYPBKdObl2MRdESjX2ShcDqncQFIkOizxTtu2Zh1IyYA9DknOUTFGCEogGKtfexu6rFS1uiE+jmbMaXfpVIdDxYUIIHl3H3GNKKG3BD3qnbmMO4dz2TvVuIOcGs98DrLSNaZqZOYGfufOHZwzDqNZNTQuRZkmKHVOYvJ+FePOvLfI2VS1iOI5VhGIMgfyWC9NFyjzaqdvki5irhsjW7XKsSwYyEx0GOfTznMnuNScipI5iomCOkEs3IR9XsXJa9dU7Ccb65NQdl4fYiHTSTD3AzDurBY16lunsnj+jHn3dan9sstIMS2GlYV4c7qCYeCiG2hOWqsk1jjOG1B1rNTycKrHzLTSplB23dnVVMd3wLkU/RXxYpmuYgyI5JB0Z5ckpyjJvnpq98RgbtjDMBD6KpWd2F5R+09kFVyu2zmH3Ed5UsZoHh0RRWIs7W02G7q+M2c0mVljopSgs+yJ2fc92nW0agWa6qrz2Qpyfn5uOS+kpU3V0+IQzNmNYKn04pj6bxisLmgmLxOPyQlneYncXhOUIhblk/OEv4nZiJp8OpbFjnJtZnaPWNDFAjVJTK8VTpVMJg7LFjcSKN3znqWNWCEjFSfGkyEUcJ2IBUzFj/yioUyT6sLdHbSGPDA7fFfq0BXeCgCnjd+LZ4tC6+kbhWDMmzn6mHWjDREnjk6V6BrcoHiN9LUDUKUszIvJm8cUXpyVQBTHSdOm1PuN7TeqqVCjK2iapcOsBaEfCP1Yu9QqkSURBHNyWrfrcr9Iqq8hSsyJb6EETg2DWWgYxmJIQ050M3QMXcf59oKNRAvpr8yhIYA0nsYZV6QhcPHwjG3oU55TYb22Gqqbs3NThDYtt09Pic0JvRvYbjsTrUwaoWst5sVc31eEfsCJ4CKmTNW0frJIMpkJU85pZ/HN9Aaqy+7S812+EIw8rnuIRnbzLseqRb3jgxEturWM0R5dk2owcbkiJqXA0YIiNytIU4fUH1VKv8fXf1wvYiFiKeWoFJVLsKCxng9MvjNx26M7l9Yycdr5JsqyxIEkN+uG5EjkajFVCWI5HuIcz7qCd8YjKk1Kx68u5dxUyyeRwfJAyPgdSoi603EXB4ybkemOVufKMM7BgQ6oNLaoGSdm5sxUMd0BjIrX3jiYvjc3b8upKYCJFhQLntBIg3MxiQ6Wok8ScRAR2kQURYTN+QWhiaxXK8R5mlbAWaRr8TNJYgjJU7NwkDGNeVq0lRqwjGnuiyl7P4ZpX3V3Nf3B9J7dNuLuFJXyj5rCSDk+Cx3fgTEGZkd0EvbO/V11xYEQhEeE60MsEgWcsvT2kTsj78LmjVh1ms6GteIqopu2pWIKvdC6lO0bRJVVqk3hRekkpoxXRihUQBsp7diEToPXhZKJq4yYVLEDam7XXtzosRmd7fpx9GpMHt4AtM6bZUGVOAx0MbJuVwRVNgMpj8XYN5bZqsFLY8qzzDWhDKHDS2OFm9WS5WhQNPRoyFYRGEJnmcq3HduLczbdhuAgSMTHMe+GqiU8zjoLh1juT1WI5nbt0qvEYbCaJgi3T+/gkiiTk/h2w4BrGrqhtxgUMbOpqSgUN0tboKneiGrA5WsXiIAy4fVzA8w9HZY5kjBbZHFnbO0zshyPMo7jXDSu21wsaVDBhEOS1PDOq9oamJtx7dSYs7NG7XHg+hCLGmY6icymFzhAMYsIktspJ+r2jc0UwKX8nM2oxio6tQlKMs4TlzmV6iKnkZhEjmyerR+afSRk1ngRGTS5mpUFZYl4TLnYIDJGdQ5hl5XOrteG7OjBaWq1ULgajYqLpKpnabfOVdZQ0/KEwDB0ZTHmP6lwy+UDVD3OWYBYrmrm1Kd+HP0MRMR0MH3ANynQzDmc98SQsmpV3JJZbMxxTBfWya4pcnKy6uA8c+L0nMjk+8jKZw5sbrp89GQyc6JRFvf4stUzqsUt9blKJJo2vqCTSXIsu7q8x4HrSSxgkd2CzB4eeb0IEhLrPTkVk6jjiGI7Za75MQZdudJGrtchyWcDBacpdX79OE3sfyE8+1nOWiSQ/JnwzxyHJyW7bVyqDubKjl4rckUsHsNjCy/vzyK2E1vQGbnCgqU7cErUAVFXxACrr1qJJC4g4qe650p0Ciltnmt0gkt+h8a7EidCiLSrFd6Pzmku1VD13tOHVJg5JdaxUo7TSFgE4/gmHGdmGUnraebNWRaXTo/NFOo7JtTJvRbNm2My7OHsLtxKv7BcC+kAgUlepZNrlhb4gUW/JCY9qhi2BNeDWNTvocqkRFuCSL1DzHYJsvJOjQWNZTZNEqnWqddtcdp9UbUQh6SDJkdmhkp7VRZpTlUXQcQSy0apJm0IaPLGyws30DF0fcqfkSqI9ZbPwTUe77PJ1XbnGCLrIVlsxLiZ1RBZq+PhCWwVKxMw9CCOE+cIQ2eekdGzSmn98a3l69QBFy035zaYK3tXJeEhRrSPMAS2IbKJEWkcQ4z06T1TLCyEAQkDnQsMfo1zK1a+pQkuhexbukKkAXE0qxUnJ6eJ8FiSG3xjJQhUWcmK++GCrgXzXnE4NatQbLJTXSCIgAyI2LSNO7ssptOoDmfSUaTRvNPGaswmF2YiTuLy026dd4miHKn1XBmqYMaJ5a52655MdjtumrUd9klirYtZSASUW0mEfgdkLFj1J4dYzKHaNZe0xbDLhmpN8Z1jh+ozFSUm99Tf86H87FlnTxRHi/LKVLFUs/Gj9luSvO/wOYAtRjOaCpOgMucaJEccYnVWrULavF/GYshDNJ1FjNB1ydzqBN9Y/s1GHLFKnisiJbmOVhm/8ru65CA1fYdxLEphouhLxTOY5reIEVo/LqL6/qZpJpnGMszrvu7t80OwMIaPzJJn0TK3eUAU2vF7WJjL8+uXvZTT5wGU93LbLK+hR4VrQix2X2bKYuZBWdA+lybsGimemmkwZzfMCca0iRSSnFjMJeVQLTJouad6HqA65s6Yv1P9GUIYg45wo16CTDCyeQ2yX8aQlIaV615Z8KpKHCxWokv+GTmjNt4RgiDeFicxpt01NRPiZLGOTlIRnF0/s85ZDdRofhTDYGZi7x2+yhmpScG5Wq2KGbzxHt+0dJIJorDptpONIE6elEWcsc3Jgt+zaIveSLXkFpmPxWWwZILdEWFgN/4kXzMGtkz1JPN28rOWnle1u3fZHyBaBy2LV4BrQiyg1mLtDKQZmavJtLs7aXW+lLffRxSS4mgq+6aTFTsXE9HQ+jmqxXKTlZFp353gJOKLuVIVNArOWUbtIZjCcRgGq/MBnJyeGh4xEoIpRINEc1xCCH0PUMynOIdqQBUcjRGJJH6FEIiD9Ufrm+IeHtPxzaajibHUH/XeRIzseTpEyx6enb/CYO06lGwXiTE7U3UQIoGGQcQSAjXgQk5CLPQELs7OOU1p/27feg7XeLTbcDEMBOd48/495E6Dbx0b7WkS3kjSk2RikTeEekEuLboynIn7WZgLi8WS9yzWSx2wdtrP7Yxi8M4z5vgeeA9rzo2b2VwUL2JUTcgCyoIu5hHhGhGLfScWcltUnbsoShSQQw2P8mBFWPKuVSrNyeixuMsi1jKvlmcVRd8Mt9x23/doiHjfltiJWye3gVxseWu7a9ASeKVRiAghLO8uZRdXkq6gIrrRImMyYZNocm4Rf2R0EDIuYWCIwdTAYnoIL3WWSpP5paq/qSqEGCAEVk324Mw+pYaLVWi3HBiFNjtHr5EhBIJzJYmwZm5p8n5ptHY4zsMwN5HujOOeXXyvmLvnmun9QClslX7u4xrmHMdi26OiPHv+6tQddLIBjkTtMr+O4+HSVkTkIyLyqyLyORH5fRH5G+n4T4rIKyLyqfT3seqevykiXxaRL4rIX7wqUiM7vGsX36HCOl2gtUb+UhAZ/1K8hTHewahyLu5T/Zm2DVLx0N1npQWnQ5jg5pwjqjAMke2mJ6biOzGAd21JPBOjcSEaxWp29Kb0zDqFru9R57O9A4fQep9EAkkZup25VjtH69rCveQq8tmhyqcksKpWMGi73SZCYRm3cmnGbCgYY0HqBWhTqO/7lAc0MnQmlhSdhzQJB5cIhllEvGtp2zWv3nubcNIQG8eg0ZIjB8ucnqudlGC2yptU8vjP/mrdylLW9Jo4EhOneIBVr8c4Dbvdp0AMEEPyXq3wqOKDihiQCk3P52uZJ+VsUuJP3ivsvJtoKhadTPb5fI2n4RWWWasrwjGcxQD8N6r6uyLyHPA7IvIr6dzPqOr/XF8sIt8FfBz494E/BfxLEfl3dZ+frd21Q/EnosHO5XvkR5a4iyNAbQctOOSAnpy7M47VvPLvUT+xWy0qX5cdQy1tXEpG4xy+bawiV4A2pdnPO3FO3KsxptR7DSvnaNs124vO2i7FiRJXEGz3iDGWWiCGl8WiOJLruJgfhdV/jckMS/HzCCEU3UU2aWYRJuMuOn6vk/zGGC2itG3wbTM+P0Afe05PTkwJCnjfok1Dt9ngnOO1u2/BqilesmBV67e101pir81JbprT4vhhPsBhHOASymLfuScUUbQs0tr9vG5Osky10M4CfgsnKY5gS3gmxW/2TK5xss+nQCxU9VXg1fT9gYh8HvjwgVt+CPh5Vd0CXxGRLwPfB/zGgafsKGF2vPCWlEo1+3aZmLD8chMOpVyduY369xTdqolRhEnKjfECzcpJc0AqizjpNKYKz5xqf5w0iiOKS9emndalSufJ76MmYtmvwougOio+AaKaW7dXT0DxSYTInAUkAhAoXFbMRsBKmauq4EdPzNAHNMKt529z2qxwjWfd3LK8nLdMD0NUNtstd9oWxOPbhpBiViLKvfOHbDUQnR+fk99Lp+7ddu64RX4oanNHH7DQhlSnKX1ZiSbZPDrPPVH0CGNqgKwwNo7BLV8PTLhpiZQCQwWnem4uiOgxK9+vuBaOgCsJMyLyLcD3AL+ZDv11Efm0iPxDEXkxHfsw8HJ129dYIC4i8qMi8kkR+STn/fRldmKBQVSLBDyygVOdwOSeGTtm7KbusmN5smXCMycgs06e7GhLIs/s+uzk1bYt0hgX0TQr2tb0FWZebCcLW+PoSlzEjzCYDsE7c8NmDCrKC1JDirEo2XhGtj23n82ZbTs+09h1jBC5ij1OWatUAD8WcPbe41ctg0a6ITLEQLtasVqfcnLrDrefe547z78Pv1rjV2va9QlB4eH5hSlPY2Cz3eJXLe/cu8fX3n2LzUrYepLvBUVEyNkQJ3Onnir1eMf4SOz2PrF15BbsGRotBD//lWenz5rw7rbjyniImiWKEO0vef3O/2z+mTicn6E6oDH/jaJKJmOa42xq69YTIBRwBWIhIneAXwR+XFXvA38P+HbguzHO46eu8mBV/YSqfq+qfi+32rT4dDoT8rPn9ybdgRQHrZhkuvRXH196l+XH1Mgd8wLpS8TyMWbtdHpGxZ3UizVXxs4Ld1JFPR9LKfisaPPoYSkyJtq1XUrLzjm21+yd/PVza5xEkgdoIl5z86QuEE2hrgfS0Edlu92y3fZ0YeCi29J1nSXsicEyYIVgVdmHwawzMXLvwX3eevAuvLDYlgAAIABJREFUgzPLRH4/EXPKkvGBVddXm8FMd3VVmPTVFUQaYKfiWRZJ52O/tOEcc2wKC/EuexEbOZsnCUcRCxFpMULxc6r6S4aPvq6qQc0n9x9gogbAK8BHqtu/KR07DHVN05oazjTSj6ST2ANHlX+ccSx1bYcaL4MpgarbN0cly6mJs4CuSVX16j1z1GnTNCAyrU+S8mOK6E5t0yza1Aq8sTtnyjGpYlXSO8Skx8i/pzIvY+xGfg65RGKyYLgxOnYYBkIca5t4b+UVzjYXiAjNqiUgnJ1dcH9zTnSW8FhFigJVYCKWHXJMeiJwVT1CgjlxHhWas7l8FAq1AndhPdSf1bX7cH0CJU4LHGMNEeBngc+r6k9Xxz9UXfZXgM+m778MfFxE1iLyrcB3Ar91KSZ7qHqN4JxY6LwjcztzncMCjBpllgc06yJmepEJbrpvMqUI2hxzAcWfISe6bds2uXlPFzhQjmWrQd/3iAiDRh48fGht+prQKCHoROGYX6Es3BDROMrPYdCRMAgluW7dP7lcoil1jSvKnJFrTPfQrNpKeZuUm1hqwYmOxjnW6zVDCAwKbbNmu93ylZde4my7KVXfYvLkzMl9RXMynzwEUtrLBO5Jsdn7YB/BqN9x77Wa/uJ8js04BVWY2QBMFKstGQu6kYpozDe2cvwJcRjHWEP+LPBXgc+IyKfSsb8F/Oci8t1YV7wE/JeGo/6+iPwC8DnMkvJjhy0hGVylURpltEmgVt4F65BhOzH9LKDVJTW7Gcu5tDzJZlANlQ7DhNByf1GuZaUhpoBKQfOWi0OUIBEnSouzmqIKnVNksHycOGEIkfV6ZXI5lpvTOc8gQi+R0DQ8dzaANGx9SCbESB97hhU4HO0QCbnb3FT7nTPVay0DAxrNonASIfbgVFhpg8aBVpKnJo4h9cwKR7MNeN+yFeOKNIDfRNxKEN/gISk3W9Q7+sbygKzQFJYeWDvH2jWEPiIx0Gngjbt3efvtu3S3ncXLROFUVgRnfRgF+jRDXfTlHaKEXQJRp8qHwp04yX3CIoyKwDznmmqeLUxbGf1SYkjB+Pu278oPQjUravNAVD4Y5XbHRC+ZBlEENO5aDI2KavqalaiVNaTM1Vm7jwjHWEP+DcsM4D87cM/fAf7OI2N1jBy6xDlcYYcZJ0R9u06fvcShFE5jujOoMpFXI9GyPCWis91u4eQFhtjTeqFZr+lCZLVqU7s2FF4dK4QokYe3lf5iy2q1InYDD7cX3O8uCLd8elUTRRpxrFcnJSYj/3VdV71rCo5Los+GiDpnGf/UqHoQIbSOC4kM3jO0KXdFdFy0qXJLDAiBBy5w3wf8quVkvaZrhRPvWbUtazHxiXasbxq9o2tb4ik81B4Jwm9/7vd4+eHb+Oe9pdpwOuYfKeORvwfbJKLCJBl3HscFa4YIMYzmzbrdIvrJeAywgLp87ZSt3T9PmP+u5ko9RyazzcL7j4HcDzt6I2WS9EdVbTCzlUz86Cx4RX3MEjwZ1673Cp7ACx6t41gSZ/a2U2mhDyizYqWAfHB2hhNl5R0uKI0KK+dpk2giIilVvmeFYy0enLHjrQrDZsuDBw/MRTxxCg7wCr6mWwmPnFk8u4d71yL4IkoUKhEtp4cL4KIgKSsXqlYEJzkbqUSIgVYlJc4Zd1iHsBJPK44VDqfRdtXGQeNHccs3BLWgtaHf8uprrxF9KhKUOIn6Paam5UQw9kyJfUrdPLZzH4snCnvZlks2u3lxpHzPkli8R5QYqzTOzz/5pX2N3L3Tbr13J3+cpkdR4ijIOgSpJ2olN9Zti1lDlFG0EUca3DGZyYOH77JZb1g1La1zrJwRBmLOwJ0uVasyJqpsEbx4fICzi3PevThjSFm4nJo4kdnaXAcnxkiIEQlC06yK7qNpW0sDiLLpu1LbFCfJ/8Kwb5xnGExX4dWe3zQNfRyQaIWjLTq1p/UNq15YqbDKOTlVofG2u/kG9Q0iVk19CJGVN3Hi/OKCd7YP2HgLdItFuJfSx2MuyprdSLklHgGmVimdcgjV2I+fZSVOz5VLZbeNfZao7NCmM5N3SUJ0xDulcgTjPE7zTWsOOXGXulxs6nHg+hCLPWbOmsrmFz92X6jNf1c5V3YiV0+GGX4LbKc6SQWOkyLOO2JKvvO1V7/OZ09P+OaPfIT3r28TBdYpRsKKMNtkcepgSDvGfeX0+Tv0Hn7rSy/xpj6gf74l+B4ULrwjaCAATZszZlsSGe8dt05OiylVnVVIV4FtH0qSXNGkKG0scGuz3fDABbqVJzawyQV/Go8QCQpbgTcuHvDu6RnPuTu4tmFYCV3j0cbxfLB3VycEL3SNYyUrbqngiLzRPeTXPv1JXj7d4E5WqE8cmBOCi+ZDolgV8bSJlCLIe5TX+zjIrAidX5vH/7J5sPf3XH9QE5QFXMwzN2cfSvM5v0uMSbE0ikWG2zJOyxuXGze5VJi5TtZbkb5HhmtELNgvdlQDICKTt653iauyl4s7y/QCM3PWv/NnzSJnlKoJo05SPkzbwbdDz0tvfp31i3d47uQWq9YyQQne2O+0Y0RVUiZgTlcraIXX77/Nm8M5/a2Wcwk0aedQGdl1q4ZmcaGm03JoqufhvSdEnZhgBY/TkBRtnga1QklOLLArjnkiRcSKFYk5JkUnXISeuw/v84FbK4IAHnMWcyZIm/jkkOR+7p1Duw5xnpdff5VPfen36Veg3ohdzZYrY8TvZTBX+hXRBfbOi4OiaR7bSl+y9/ycWFw2lyr8SuSzyKgjOQLHOh/I5IrMlE1Ojrg+CQ7j+ugs6p16qaMWlFTZdLY0WZYmya5IsQcPkZL/cgeWJkXFGtZOUoDV7Ewh1u9053zpj19CVxYfIgpCKhKUTGSNCk4U0YBfN7x69y0+84d/QNfYLi2itCqmGxDLKeEVVDyrk3WKFrV3jGJciyb3bSMqhluD0KjdK8GsNK04Wufpus4C0SK0KjTikBCTTmX0z7j78B73NmecD52l4Qs9jVgUaUxFgvq+T1yaEp0wOPi3n/wN3nrwrlmDRJN7uRanrHq8sifpvKr6fPwn4sVC8Fh9T318Pjck7cKu6JCmf4KMXpakvyxBJf2OS9eUe3R8RsaP2syd5tsEh8vWQ3Vd+dO58naqYH1cuD6cxZJycfEFo9XLKNR/vHaeHl9TbMOj4FGeLAt4VWn/chX1OrlqVLMiaOMIIYKAaxxv3up5oPf5hX/7L/imFz/ID3z0z/D8yS0ISSRJjlpvvfUW9x7c5V99/pM8OD8jrBsuTszw1kiuj5pqejhP6y0mY0AJqvjGk0PLU4+lRedSwR4h0iOrxiqPiVrglnZsGzhbwbm3dH8qQh9DqlVq7yKiBK985eweD+5ued13fM833sFvtqz6jvu3byEMPL+22JE7zYp3b8OnX/0Sv/Hp3+Wr2zcZ3rciiDIMgbBORJVYKom7zGUV/nlqFrSh2p0fRU7X3ePjEOv4ORcnJtfsOz6dDvUz9vldZLFCxI0m2Wx5yfdkncaSDkSn7U0OMmMqMsHQSzbGK8L1IRaXQS5wu/f8AlGYiSxLu4ruXH9J52bF1+w60wukHzGpNhW8ywl8I/HWik2I3L3YsHnra6w/87t885/6MC8+9zy3T+7Q9RsuLi74whe+wGuvvcbrtyP+hRO725kTVT8MOOcYBAY1ZWdQtZiRpGdxzuGdtzT8OYAt7ZhGOBRpjegQjahGF1EcQYStU3qvJl44UzrnRDEiozJUbzW8fbHh/K2X+eD7P8CHnnuRW+sW31i1NpccvTb9hl/79V/n81//Kq+ev4vcXtOHgUAktr6kBtDKV8Ryaewf211u8vhFUe/gyjLxqXUGmvpxzq2O3w9vSHZf/XyfnrWgC7MbJu886iEOPCf5f4i6vRajx4XrQyzmCqLcYVmWrankvs5YosjHQp5AKQPVjjq1wilX9ir276RQyuHJ4lNtUsGyUTtBB6UNkSiRbi0Ehd95/Y/4zJtftWCvbkjmRUcgIKfCQw+tKGs8OgwE7yyJLtCjtGH0Ih0YzKegTUmKk39DDlkvCq+UINgNESWy9g1bNa9JaRzn2ws6guHd97jGLBkIDOIQCayj1T7rRdE7ngsN/MoXPsntaI5Xq7BKIeYdKsr55oLtidCvHPG2p3MD6p2ZVlHU5Wzrqc9dlX1szzjWO/h8MWaoF/cxFrEl7mOprfx7vGbqcl27zM/bm7QrSdmjUJtRJSnBah8KqvD2JRj1M5oVWTxpLcP1IRawLH7kTtyjx5jAJVzBOGEuv+YQfvXOlHM7ypwrSUQiu5MX2VthaE2u75xwkXQOftUyxB4ISGuFgVvXIEMkhIhftXQuKSHTo5rGoUFLCcWcZUrq+qR+3BG998XCExuIwQpHRycMqmxD4N75QwaXKqt745aUAXxD503OcylIuCcQBII44qly1nVWocwF0Iem9FRFnwNymUcROhFohKghRb8qIReZqirF1R60c51V7vMpwTiOEByCyyxo0+86mQtL1414TvEoYke5SSgWkYmksbBpJahJwTw9oL3HaLp/EnB9iMXy1vDYipkrW0lqWXXPfTlewkoO2nUClqYgpayjmhSqihchOFAxzoAQkZM1BFOmhRBwrSlDo7PYj9v9uEFEIhtRtPVjGQJNFggZlXcWterwjdC6ttpxxnwMIQRoPDEEy4iV8NyEgQfdhtAIg8lQaHIC7Bowh0NhmwspOWfcBYEHCt5bQuDBAbKCMCQNoClvW2mSMtb6QlxDh7JK5Rh2dt8ZAc7jWIpIVwRk3zS5yvjnHXzfPfuOX8naUkNdh1RknEC7Dx65iokOZEGsrgma5R3gSXEY14dYZJhT5cpZRZOuINdDnZuhlmbMkjya8z3U7r7jvZVjWBYrJzuA/VZS8tw0iKWGhVCyOimg3naKISo+BXJ4HOq95TLA/Pq9d6V9h6XIUzegLu3OOLwq9EPBXSWiTglE3h3Oef7Oc8TOMlahztLy43HOFn+MAyA47xi8Y6UQ1DEMG+Km4ytf/ypfvXiT7Rqchy5Vh/YB2kGRIKik0otJedxkuT7hIhFWKkmUM+sNeDSl9gvexErzmrB36cj6nj2colg7msSl3cmfnbiqMa4Uzgp7S0DkMZ7mrJzissQ92AFTUCqM/hyFI6pwLDqVUJiGydxDKT4YRYSocU5cBxZ7hNRxHwe0NZUoMnXcejS4fsRiz6Kf7jYL9+3IgtNjR5lNrwK1/DhXjM3xgSKCAKngsRuLM1NNDBkTrGYvUhQLcGPkVGqFm6ry7uac09u3CbHjhVt3iIPSeZAU+m2VvMx7MoZoLtZJl6JOuL85543777BdKUPLqOvQnMouVlYGt7twEr4Wl2AcTFKnlnPjr5EOZ/Z5cXc8cse+ypiOC3p/rtar+uvMHlA3tHzNbA4v6UcyHjUuccwwnVjY/ToMa8AXnJ7EtL8+fhY1ZLZs8dSeztkzwQ7dv6NsOtjxcjUz7AGCVZ4vFWst6Y8ctqETrXYuoZg/NXE3edLff/CA882FPUdh5U25iWeaZEdBugB9IHY9Yeg4OTnhD7/6Ej0BWjOXqoCLShNhPROIA4EocZq9o/I1sfonUOfLKH4emeiwK2cfgv3mzb03TP8SZP+DxyIIVTsTXPJzDrlu7+NSltqvoFTkm4gsB3Q0jITiScH14SwqKlm6YI/caOztnCWsFudVJ8I+fYlI8qmYsrF7n5sX8B7IcrndklPn5dgQpbDTiYgMVXas7K2ZrS1RLJw+qhX3+cw7X+OV/j7f8eFv5pbc5pasaZJIFCQScg5OL7jW4yVw7gOvnd3jC19+iYdNx31RLpocdWkV01QjfVQGrfo8TdiYKo6HdCimMGqfzofUHyoRSe9JFc+SWfZJkts9HBtzy0i67lKuQqbOSuPtMzOoMrE+XKaHEJExFmaCq98RbTNhiTvLTct8rtstZ6Wutj57T7FJYW1XqRTL+dn7PwEfzmtDLI7RQF927cKFZcD2enlWk4T5wBel0p7OnhOmJRFq4ViROEjkQS2/aMYxJHbdVwl5S5halpFjLEQsCpydQh8e4u69wb/z/PtR77jTNOaejRIcNM4RFTYEzruHvPrOG3z+tT/mre1DOqfEtaNzWLV1cQRvHp+WtkGmeAul+E8230rKrxA0JfBPeUOEMeHvxHGp0itM+usA1JaFSy4EUrCdxkLI9+ofuJxgzGFuiTkKt3zPEa+wyKHMOGhVKwix1GImkgpTj9FHhGtDLK5qtSius3mzqwOGakXXgvZ453m1Vrr+rLXQ4427vzNO6d65ljrrMGql20ThpDmPQ9JDpMOhSuuvYpaFMkGzshfbmQcxXcQrb7/Gr73xDt9weof/4Ju/g9vr2zTiwDt6J2z6ji/8wRf56v3Xedhv6VaO7sTROwWneFFcSGHjadFn/M3xTMbJl+u0Vv3sEJSQHaPN7buCEvNREei6Hyb9P/kuSfZWFiueL4GO2cNkwffhEMzny27TOrlGc/m06vyehvduYFPfjYX77KZdblZkJ2BuZz09ptgF14hYALuL7JLBOvS7xPkf9WCKBWPayIEOPnLSLb5DWSBKjhbch2mZjBk/ZZLbwMQTZXCCR+m95+52y8PznrOvfIET19A4RzcM9GFgGyyJTrcSBufoG6V3EU1xJzk+o8ZG0vOLG/KMI5jrVgzNNJbI+PuQvmlJZ3RFrmMRjphLk/NHXD9d2JW1rlqgiz4gT0R/UHERS/q36j1qfJ7Ek68VsTgGsoZ+Bw4owPbJnxOuY97OPj1G9bm0xA9OymweTSbTTKDqsPa8c2diknF3knYOETT7k1ewyenniLhGkEG5Hx7gesPX3MIjAxF5rmFQM1+qCEFAvRaHLfF2LC98x7Q+SVn49bvm37p7TuuOOmLBHDXBjyQci67cibGZekg+CuyKCZdyyIfmFwc2yszJVDlS6nlrKVR0cv2h5zwKXEtisU+/MH6GSwfE5HtA/I6dPbe7d5IsdfTC4Gl1jbA7wFor7+oF5BLBq0WKcn/1TDeey7u51m1kgqGWwzMrQ2PjCF7pVUkumDjNFejNnT22jjYILplkg6foEWL240lExkWITSUGZCI3YX1H3OtJvBOnUPdDVr4V6W+2UB5nos85zdlOX7/H5Nnz3/MFeIRSdUc3M2/nUtT3c5mlzYSLq/A8uHktOXtdEa4Hsdgrpu3Z3a+wq+j8956BuIrGeK6bONT+hGDMG5k/rrDi+7kk8iSv06yJ0MSAJAcsUUXVfCuGYSh4eY3FnHkhDtFYfCjyQs9FnFKwLD5a4NiQrEK1vDzBUpXsUDTB9UCXzgn4UQuq1nccAqmI2+yZ5UjUZfFz0swRFpdFNB+NWzn6vqr/p56sC9yFexJCyHUhFkw10RkuY+n2dcGxitIlpdJoxuRSlnnU6svusaVrJ8jPiJ7s7sp7Hpo+63WrDK1lT3BguSeSHT6n5lcdiDhbv2Eo0YnRCzhHSE5akw1IBByEfank63eQStkxJnCYtHWVhXdpP0xwOKrJRDyOvLbcMhO1jr9x+fo9YsZyv4wOeoeuUw3IXqVvSCHQf1IUnLLArsKsw2O5ViS7sI5scO36fWg+1ASiUGSpEuhoqiyeF4ivCICttNkiGL0tDWWd4l2LK4U3n+24S4oqbMEX83n+TOHcxgflsH3zg8h95FpHI2JOWQkfh9VIDRrBtwiKtuYjEXOsB0r0uU+N4xiEtPjdzuLfEauS6EJcGDcqs6IAuNGUmauLT3bIaUqCcQPIcvu0X0ZCVOX5HrukYsdnuM9FC007dRkQJqBypB/jbHGOCvfjqZW9UxVIKUIuKDBuFlreGyzOyH7HZDFjvP8x4Xp6cGY4ghrOXWJ3YKm+KVPuYyQEC6arOeex51k7Zq8nMDhzPPLn6Bou088ZPtn5qSakGfYp0hZ3uCIeLeAz/7507QGYcGY6zXJ14Kbldh4DLnPCelx41PYmeC2M1b7xe9L4w3XhLPZBLRdXk2rsiDC6tSbYVzn70IIvLG+Wy/M1meOo7tV6u0pH0Fl2o5qaC4UQHc0vl5pMM6VU3vFL7EXFgs8UWAGFVI5QVUsovXNNSpIzdRkfd/3Z5FwiSDWHUOkqjMOYiSAz7mCJoDrvFiuL7bUMHCRG1W48e/6xMPehmJ08Do/Je1LmsiDLXPQePKbgqs/9Csv3glDAdSUWSzqDSqanUhjWA1ts3Pl2ObCDTjzaHl9TXNBMC2dH1j1qAI/EYy4Pz98NRi/LRFBybEE9T/fGySw8yyXcJhhO6J9UJxe4kFoncxk3Myc4M9i3IZRzOTfE5MTy9XtxmB0vc+sKi52F7FZmtdjl9C5BLrVzQH9X8BxFkicN11cM2UPBr0I198VzHFJgjkrLuHP8UjwXzx97/TSBK1wSaLWz29antMShLCrE6k0/iw0TfUTVXkVsVS09wLSx/Xgdq2guhH6Gxz78Yf/YjjfLyNldhsaB+bDv2VcuOPyoCsaC03ypLoXpV4mY3gO4HpxFUorVSixE0BRIJQix9lp0NbWvOY55s1qossyP2027uKR4hvGSmcUks92FFU+4MC6qyXvNFZuLMGPB86LZx/JWMr491oPmRDOj2JDjTkTE8mqU92HMip7iO6TxU5FrpgAkVlGw+Trnxvcrx0bcMzES5xZ0EYcI9eHJXnbRS9mFAwR+3r8zDqLGad/z9+lxpFbmHoCaA95/0R6xcPbcJXf2RzX77oNjqqifiMhvicjvicjvi8h/n45/q4j8poh8WUT+iYis0vF1+v3ldP5bLkdjWUlZV8rOisyd6/Y4m9RsmRCxxCmBkQLXf+aFNCZSZfKsXR/7egccxSLNcndFpI6GpR3eXmQkUPn3zj1xXLhMr6sdp4o3aCo2VOtTJoSixqM+npXF9WKb4OMpwU9zbrB+v513FEpZxTQO5pEmaSW5yd/iAp4MUZUKf+m5B8TCfQrW+SJc3L135uZszlai83xTWZz/OvtDdo/pno1q4ffjwjFiyBb4C6r6HwHfDfygiHw/8HeBn1HV7wDeAX4kXf8jwDvp+M+k694T2Kf5rb3WlljZSy0oC20+IoK7C2oHLtFTXMW6sDNZMd8J70yhKZb0Jua0f5M/Uv2O3Qlvr7JHHCjfZsWYlv7q80uwZzwPPX9yzeL63c95XhWOcxp7/Ht2we0Rya4WHPe4cCmxUIOH6Web/hT4C8D/mY7/I+Avp+8/lH6Tzv+AXDZS9UR1Vf6G6rjumcxL+gX7HUYPvaV7dl90b6dPRJBYfZ9/ZvFkqZnFpo8gFPM28m4iyi6HlO6p3jnjPklEk/urFEBaeF71fa98rg7UmQPYUYtiD+Gciz9zLqdclvUwmdtIx2uGqp475VUqgrGHIxBy1TW4VLrZM1f2FSmeW9R22qnn1c753bk7/Uvnk69MGavZM58EsTxKZyHGF/4O8B3A/wr8IXBPVZM3DV8DPpy+fxh42fDVQUTeBb4BeGvW5o8CP5p+PtSf+tdvz6+Zw6HpeCxt1dnnAfiAXoLPU4YPkPC56j6yj35d5f59+Dxu248DC88tfXTgmr1t6ez3kc+8Ej5XuPeoc8dek87/6WNw2QdHEQs1Yf+7ReQF4P8C/r3HeWhq8xPAJ/JvEfmkqn7v47b7pOAGn8Nw3fCB64fTdcTnce6/kulUVe8Bvwr8GeAFEcnE5puAV9L3V4CPJOQa4H3A24+D5A3cwA08ezjGGvKNiaNARE6B/xj4PEY0/tN02Q8D/zR9/+X0m3T+X+vT0sDcwA3cwHsGx4ghHwL+kRR7Fr+gqv+3iHwO+HkR+R+A/xf42XT9zwL/WES+DNwFPn4kLp+4/JKnCjf4HIbrhg9cP5z+ROEjN5v+DdzADRwD19fd+wZu4AauFdwQixu4gRs4Cp45sRCRHxSRLyb38J94Rji8JCKfEZFPZfOSiLxfRH5FRL6UPl98j3H4hyLyhoh8tjq2iIMY/C+pzz4tIh99Svj8pIi8kvrpUyLyserc30z4fFFE/uJ7gM9HRORXReRzKezgb6Tjz6SPDuDzTPpInkZYxq5H2NP7Azzm4PVtwAr4PeC7ngEeLwEfmB37n4CfSN9/Avi77zEOfx74KPDZy3AAPgb8c8zX8PuB33xK+Pwk8N8uXPtdaezWwLemMfVPGJ8PAR9N358D/iA995n00QF8nkkfpfe8k763wG+m9/4F4OPp+N8H/qv0/b8G/n76/nHgn1z2jGfNWXwf8GVV/SNV7YCfx9zFrwPUbuu1O/t7Aqr665j16Bgcfgj439Tg/8F8Xj70FPDZBz8E/LyqblX1K8CXsbF9kvi8qqq/m74/wMz3H+YZ9dEBfPbBe9pH6T3f07CMZ00simt4gtpt/GmCAv9CRH4nuaEDfFBVX03fXwM++Azw2ofDs+y3v57Y+n9YiWZPFZ/EMn8Ptns+8z6a4QPPqI9ExIvIp4A3gF/hCmEZQA7L2AvPmlhcF/hzqvpR4C8BPyYif74+qcarPVMb83XAAfh7wLdj0cevAj/1tBEQkTvALwI/rqr363PPoo8W8HlmfaSqQVW/G/Oo/j6eQFhGDc+aWBTX8AS12/hTA1V9JX2+gcW+fB/wemZb0+cbTxuvAzg8k35T1dfThIzAP2Bko58KPiLSYgvz51T1l9LhZ9ZHS/g86z5KOLwnYRnPmlj8NvCdSWO7whQtv/w0ERCR2yLyXP4O/CfAZ5m6rdfu7E8T9uHwy8BfSxr/7wferVjx9wxmMv9fwfop4/PxpGH/VuA7gd96ws8WzDv486r609WpZ9JH+/B5Vn0kTyMs40lqiB9Ri/sxTJP8h8DffgbP/zZMS/17wO9nHDD57V8BXwL+JfD+9xiP/x1jW3tMtvyRfThgmu+cKuAzwPc+JXz+cXrep9Nk+1B1/d9O+HwR+EvvAT5/DhMxPg18Kv197Fn10QF8nkkfAf8hFnbxaYxA/XfV/P4tTKH6fwDrdPwk/f5yOv+yXV4gAAAAU0lEQVRtlz3jxt37Bm7gBo6CZy2G3MAN3MD/T+CGWNzADdzAUXBDLG7gBm7gKLghFjdwAzdwFNwQixu4gRs4Cm6IxQ3cwA0cBTfE4gZu4AaOgv8Pzg1TDYnl5xQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证结果为:  [{'category_id': 0, 'category': 'paper', 'score': 0.7166558}]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证结果为:  [{'category_id': 1, 'category': 'rock', 'score': 0.98926264}]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证结果为:  [{'category_id': 2, 'category': 'scissor', 'score': 0.8606307}]\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\r\n",
    "import paddlex as pdx\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "model = pdx.load_model('output/result/best_model')\r\n",
    "list = ['paper.jpg','rock.jpg','scissor.jpg']\r\n",
    "for i in list:\r\n",
    "    jpg = i\r\n",
    "    img = Image.open(jpg)\r\n",
    "    plt.figure(jpg) # 图像窗口名称\r\n",
    "    plt.imshow(img)\r\n",
    "    plt.axis('on') # 关掉坐标轴为 off\r\n",
    "    plt.title(jpg) # 图像题目\r\n",
    "    plt.show()\r\n",
    "    result = model.predict(jpg)\r\n",
    "    print(\"验证结果为: \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 5.2单张图片预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-08-13 21:09:52 [INFO]\tModel[ResNet18] loaded.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "验证结果为:  [{'category_id': 2, 'category': 'scissor', 'score': 0.8606307}]\n"
     ]
    }
   ],
   "source": [
    "model = pdx.load_model('output/result/best_model')\r\n",
    "\r\n",
    "img = Image.open(\"test.jpg\")\r\n",
    "plt.figure(jpg) # 图像窗口名称\r\n",
    "plt.imshow(img)\r\n",
    "plt.axis('on') # 关掉坐标轴为 off\r\n",
    "plt.title(jpg) # 图像题目\r\n",
    "plt.show()\r\n",
    "result = model.predict(jpg)\r\n",
    "print(\"验证结果为: \", result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 四、效果展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\r\n",
    "#-*- coding:UTF-8 -*-（添加）\r\n",
    "import numpy as np\r\n",
    "from skimage import io\r\n",
    "import cv2\r\n",
    "import random\r\n",
    "import os\r\n",
    "import numpy as np\r\n",
    "import paddlex as pdx\r\n",
    "\r\n",
    "# 通过判断玩家手势 play 和电脑手势 bplay 来更新胜负情况 p 和 b\r\n",
    "def updateScore(play,bplay,p,b):\r\n",
    "    winRule = {'rock':'scissor','scissor':'paper','paper':'rock'}\r\n",
    "    if play == bplay:  # 玩家和电脑所出手势相同\r\n",
    "        return p,b\r\n",
    "    elif bplay == winRule[play]:  # 玩家胜\r\n",
    "        return p+1,b\r\n",
    "    else:  # 电脑胜\r\n",
    "        return p,b+1\r\n",
    "\r\n",
    "model = pdx.load_model('output/result/best_model')\r\n",
    "\r\n",
    "shape_to_label = {'rock':np.array([1.,0.,0.]),'paper':np.array([0.,1.,0.]),'scissor':np.array([0.,0.,1.])}\r\n",
    "arr_to_shape = {np.argmax(shape_to_label[x]):x for x in shape_to_label.keys()}\r\n",
    "\r\n",
    "options = ['scissor','paper','rock']\r\n",
    "winRule = {'rock':'scissor','scissor':'paper','paper':'rock'}\r\n",
    "rounds = 0  # 进行的轮数\r\n",
    "botScore = 0  # 电脑的得分\r\n",
    "playerScore = 0  # 玩家的得分\r\n",
    "\r\n",
    "NUM_ROUNDS = int(np.random.randint(1,10))\r\n",
    "\r\n",
    "cap = cv2.VideoCapture(0)  # 打开摄像头\r\n",
    "ret,frame = cap.read()\r\n",
    "cv2.resize(frame,(300,300)).reshape(1,300,300,3)\r\n",
    "model.predict(frame[50:350,100:400])\r\n",
    "\r\n",
    "bplay = \"\"\r\n",
    "while True:\r\n",
    "    ret , frame = cap.read()\r\n",
    "    # 按空格进入游戏\r\n",
    "    frame = cv2.putText(frame,\"Press Space To Start\",(160,200),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)\r\n",
    "    cv2.imshow('Rock Paper Scissor',frame)\r\n",
    "    if cv2.waitKey(1) & 0xff == ord(' '):\r\n",
    "        break\r\n",
    "\r\n",
    "for rounds in range(NUM_ROUNDS):\r\n",
    "    pred = \"\"\r\n",
    "    for i in range(90):  # 每轮游戏在 90 次循环内完成\r\n",
    "        ret,frame = cap.read()\r\n",
    "    \r\n",
    "        # 倒计时，此时玩家考虑出什么手势\r\n",
    "        if i//20 < 3 :  # 循环执行次数 i < 60\r\n",
    "            frame = cv2.putText(frame,str(i//20+1),(300,200),cv2.FONT_HERSHEY_SIMPLEX,3,(250,250,0),2,cv2.LINE_AA)\r\n",
    "\r\n",
    "        # 预测玩家手势\r\n",
    "        elif i/20 < 3.5:  # 60 =< 循环执行次数 i < 70\r\n",
    "            pred = arr_to_shape[np.argmax(model.predict(frame[50:350,100:400]))]\r\n",
    "            \r\n",
    "        # 得到电脑所出手势\r\n",
    "        elif i/20 == 3.5:  # 循环执行次数 i = 70\r\n",
    "            bplay = random.choice(options)\r\n",
    "            print(pred,bplay)  # 打印出玩家所出手势和电脑所出手势\r\n",
    "\r\n",
    "        # 更新玩家和电脑的得分\r\n",
    "        elif i//20 == 4:  # 80 < 循环执行次数 i < 89\r\n",
    "            playerScore,botScore = updateScore(pred,bplay,playerScore,botScore)\r\n",
    "            break\r\n",
    "\r\n",
    "        cv2.rectangle(frame, (0, 50), (300, 400), (255, 255, 255), 2)  # 玩家需要在这个矩形框中比出手势\r\n",
    "        frame = cv2.putText(frame,\"Player | {}   :   {} | Computer\".format(playerScore,botScore),(100,450),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 玩家得分，电脑得分\r\n",
    "        frame = cv2.putText(frame,\"Player : {}\".format(pred),(25,25),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 预测的玩家手势\r\n",
    "        frame = cv2.putText(frame,\"Computer : {}\".format(bplay),(300,25),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 电脑手势\r\n",
    "        cv2.imshow('Rock Paper Scissor',frame)\r\n",
    "        if cv2.waitKey(1) & 0xff == ord('q'):  # 按 q 退出游戏\r\n",
    "            break\r\n",
    "\r\n",
    "if playerScore > botScore:  # 玩家胜\r\n",
    "    winner = \"You Won!\"\r\n",
    "elif playerScore == botScore:  # 平局\r\n",
    "    winner = \"This is a draw\"\r\n",
    "else:  # 电脑胜\r\n",
    "    winner = \"Computer Won!\"  \r\n",
    "while True:\r\n",
    "    ret,frame = cap.read()\r\n",
    "    frame = cv2.putText(frame,winner,(200,150),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 显示胜者\r\n",
    "    frame = cv2.putText(frame,\"Press Q To Quit\",(200,200),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 显示'按 q 退出游戏'\r\n",
    "    frame = cv2.putText(frame,\"Player | {}   :   {} | Computer\".format(playerScore,botScore),(100,400),cv2.FONT_HERSHEY_SIMPLEX,1,(250,250,0),2,cv2.LINE_AA)  # 显示玩家得分和电脑得分\r\n",
    "    cv2.imshow('Rock Paper Scissor',frame)\r\n",
    "    if cv2.waitKey(1) & 0xff == ord('q'):\r\n",
    "            break\r\n",
    "cap.release()\r\n",
    "cv2.destroyAllWindows()\r\n",
    "\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "需要在本机运行程序\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/4b63e4be56194edfa0a086adf7d5a2b42719f847748f4242a5c03037fe5f2295)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 五、总结与升华\n",
    "做完这个项目让我学会了使用paddle框架，同时了解了图像识别的相关知识。项目目前仍然存在很大的不足，在识别手势的过程中，识别出石头的概率远大于布和剪刀。希望有能力的小伙伴加以修改。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 个人简介\n",
    "欢迎一起学习交流\n",
    "https://aistudio.baidu.com/aistudio/personalcenter/thirdview/282096"
   ]
  }
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